I have no issues in Battlefield 4, a game by the same developer thats several years old. So it begs the question: Why bother putting out patches if you can't proceed to break everything and force us to wait a month for the next patch to hopefully fix it?
Sorry to hear you're having difficulties. Have you reached out to EA support and if so what did they say/do?
You're not alone. Not saying people don't deserve to go on vacation, and I hope the employees at DICE/EA are able to balance work/life, sincerely.
However, releasing a patch that has resulted in Killswitching one of your game's most iconic Villains, making game-changing saber-stamina alterations that no one was requesting, and as you point out, and QUITE LITERALLY, making the game utterly unplayable due to their faulty ping-site/empty lobby situation is deplorable at best. This is an overnight, all-hands on deck, 5-alarm, red-alert situation that needs fixing three weeks ago.
Honestly, what it shows you is how strong and vibrant the Star Wars community is; that a game so terribly cared for is still alive and well (despite all the "dying" threads). Let it be known though, it is the Star Wars community keeping the game alive despite all the destructive patches released one after the other. Most devastating bugs from release are still present and appear that they will never see a remedy (e.g., Vader choke bugs, Force ability bugs, etc.).
I'm glad @F8RGE takes such pride in his efforts because they are well-deserved, and we need someone to fight our battles at the corporate level. But his hands are beyond tied with all the b to the s that we have no comprehension of.
Last, the DICE-version of this game for testing is absolutely NOT cutting it. All future testing needs to be done on the client side - there is nothing more imperative for the future of this game.
Well if you love playing this game as much as I do then I'd be on the support line to see what's happening and see if they can get it fixed so you can start playing again.
Also, I know you said you tried the ping-site issue fixes. But I tried a slightly different approach, and it genuinely worked for me - hope it works for you as well, friend.
Well, through the latest update, they fixed the ping issues. They fixed mine at least. If it's not working, then ur game is poop and broke. Come on man! It's EA! What were you gonna think? That the game will be a massive success?? I'm sorry man, they fixed the ping issues, but if it's not working, then it's too bad. If you live on the East, stay connected to Virginia.


Early this morning, I left the inn at Tokiwa which was the closest point to Katsurao Village. I headed to Seseragiso hot spring facility of the village, where the trail to Mt. Gojunin started. On the way, I could see Mt. Tatsugo as shown above, which was located within the village, and which was nicknamed as Katsurao-Fuji from its beautiful cone-shaped landscape. This morning the mountain was reflected on paddy fields just restarted to grow rice after suffering from evacuation.
In the back of Seseragiso facility there was a trailhead to Mt. Gojunin as shown above. The actual trail started from either behind the sign board or right hand side of the board.
The trail went through the fresh greenery as shown above.
In about 1.5hr I arrived at a large field on top of the mountain as shown in the pictures above.
There were two peaks on top area, south and north. The official summit (883m) seemed to be on the southern peak. I looked for the point and found a similar one as shown above. But afterward I checked the GPS data and I believed that the true one existed somewhere else in the neighborhood.
From the southern peak I could see the beautiful northern slope as shown above.
On top of the northern peak there was a large stone as shown above which was said to be used by a famous General Sakanoue Tamuramaro more than 1200 years ago.
I could see a view of Mt. Tatsugo or Katsurao-Fuji from here as shown above.


In the traditional software market, it has often seemed that the only thing that a vendor felt they really needed to know about a given customer was how to get their signature on the initial contract. The bulk of the profit to be realized (and the source of the Sales' commission!) came up-front from the sale of the perpetual licenses. After that had been accomplished, the barriers to switching were presumed to be high enough to keep the customer tied to the vendor for some time.
In the SaaS/Cloud sector and business model, there is no burst of up-front profit, and the barriers against churn are much lower. It can take many months, in some cases as long as a year or even more to recoup the customer acquisition cost. A customer who elects to end the relationship before that point can instantly turn what appeared to be a highly profitable deal into a dead loss. As a necessary result, the Customer Success Manager at a minimum must know a great deal about the customers' business, and especially about the customer's expectations and usage of the vendor's application, in order to have any accuracy of insight about the real status of the relationship.
The beginning of the quest for knowledge is in determining what you need to know in order to function as an effective CSM (Customer Success Manager). What drove the customer to engage with your company and application? How does the customer measure success? Are they tracking their own progress towards those success goals and objectives? Are their individual users appropriately moving up the adoption curve of the application's feature set? All of these core questions must be reliably answered, and on a continuing basis, but the need for knowledge doesn't stop there.
Some of the answers will come from conversations with your customers. Others will be provided by your application feature usage monitoring resources – but here, too, you have to know what to look for. Which features are core to the application. such that there is something very wrong when a customer is not using them? If your monitoring tools haven't delivered enough data to use in this regard (or even if you think they have!), go talk to the Support team reps. Ask the customers themselves what they consider to be the must-have features. What does the Sales team report as customer hot-buttons? How do prospects talk about their expectations? What does Marketing say about competitive analyses of the opposition's products?
What you don't know can very definitely hurt you in the game of keeping customers and increasing per-customer profitability levels. So can what you think you know. Cross-check the data and responses you get to your inquiries from different directions. If there is conflict, dig deeper. Why would some customers or respondents think that a given feature was vital and others not? Look for patterns, and matches with verifiable customer behavior. Look, too, for what doesn't fit – and ask why.


The plan, by the end of the book, is for you to have built a fully functioning Rails application—and, in this chapter, we are going to start that process. We'll do it by building a smaller training app—something you can refer back to and experiment with as your knowledge of Rails grows. That way, you'll get a feel for how a Rails app hangs together. You'll also become familiar with the various files and directories that make up a standard Rails app.
It's common when building an app for the data requirements to come first. In fact, we'll be doing exactly that when we start our main project for the book―planning the database schema and creating models are the first tasks.


Our philosophy is simple: fewer principal brands in our portfolio in order to maintain an intimate understanding of the wines and brands we represent, and fewer buying groups to develop strong, long-term relationships. Much of our portfolio is limited release, allowing us to offer exclusive supply as well as the flexibility to source specific wine varietals or styles from Argentina.
All portfolio tasting are by request or invitation, to respect the wines and principals we represent.


text:
Jarvis Landry burns the Dolphins and explains why he's excited to play with the Browns in 2018
By Oliver Browning
Back in March, the Miami Dolphins traded Jarvis Landry to the Cleveland Browns in return for a 2018 fourth - and 2019 seventh-round draft pick.
The trade signaled the end of Landry's four seasons with Miami - and it's fair to say they weren't the most successful years.
Despite playing every game of each season - and starting them all in the last two years - the wide reciever never really got a good taste of success.
STRUGGLING FOR CONSISTENCY
The Dolphins best season came in 2016 - when they finished with a record of 10-6. But after only finishing second in the AFC East, their journey ended in the wildcard round of the playoffs.
That year was the only time Landry and his team finished with a positive record. But according to the man himself - there may have been a reason for that.
BURNING HIS FORMER TEAM
Speaking recently to the NFL.com's Marc Sessler, Landry explained the problems that he faced while in Miami - and it's to do with their quarterbacks.
The 25-year-old explained that he believes that QB play is very important - and suggested that he's much more excited about the Browns prospects, compared to his former team, when it comes to that position.
"The quarterback play is so important, which has got me that much more excited about Tyrod (Taylor) and (first-rounder Baker Mayfield)," Landry said, per The Big Lead.
"The way that they're throwing the ball out here, it makes me that much more excited. It's a lot better than what I had in Miami. I'm excited about that."
FIRING SHOTS
Brutal. Although Landry didn't name any names - it's probably fair to guess that he was talking about Ryan Tannehill.
He was throwing for three of the four seasons that Landry spent in Miami - with exception to 2017 - when Tannehill missed the entire campaign.
The 29-year-old tore his ACL and required season-ending surgery before throwing a single ball. He was replaced by Jay Cutler for the year.
As well as burning the Dolphins, Landry is clearly looking forward to what's in store with the Browns.
After drafting Mayfield as the first overall pick of the draft, things could certainly be looking up in Cleveland.
And after finishing the 2017 season with a record of 0-16, really, things can only get better.
Especially as they've made a statement of improving the offense by trading Landry and drafting one of the most promising quarterbacks in recent history.
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redpajama_set_name: RedPajamaCommonCrawl


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text:
All You Need Is Love, Love. Love Is All You Need
Leave a Comment	/ Your Home Journeys / By victorkang
They say that only single parents understand the struggles of being one. Suhardi bin Ahmad, 50 and Norlina Bte Salim, 49, know this all too well, having experienced it first-hand before. But that was also what brought them together.
Suhardi was a widower, having lost his first wife to cancer in 2012, while Norlina was a divorcee who had divorced her husband in 2008. They both had three children to support.
Read: What happens to your property after you die?
Fortunately for them, they were given a second chance at love. And though being married required one of them to give up a home, the sacrifice was worth it.
Suhardi and Norlina were introduced to each other in 2013 through a mutual friend, who was Suhardi's colleague and Norlina's ex-schoolmate. Small world eh?
Norlina had reservations about dating and marriage at the time. "I felt like I was in a comfortable place in my life because my kids had already grown up, and I had no intention of getting married," she shares. "We just wanted to be friends at first."
The duo first connected over Facebook, before texting on WhatsApp and Facebook Messenger. This was followed by phone calls, with Suhardi requesting for a meet-up soon after.
"My Facebook profile picture was a cat, so he asked why I didn't have a personal photo and I showed him my picture," she laughs. "He initially wanted to meet after returning from Europe. But after seeing my photo, he requested to meet immediately!"
Long story short, love blossomed between the two and they tied the knot two years later, in 2015.
Read: Let's Talk Law: The Singapore Women's Charter and Female Property Owners
A tale of two houses
Once Suhardi and Norlina got married, the newlyweds were planning to keep their own houses in Woodlands and shuffle back and forth.
Suhardi bought a resale four-room HDB flat after his first wife passed away. As for Norlina, she was the third owner of another four-room flat, which she purchased in 2010. Both found their respective agents on PropertyGuru during their house-hunting process.
Looking for a flat to rent or for sale in Woodlands? Find one on PropertyGuru now.
Though they were comfortable living in two houses, HDB rules required one of them to give up their property. As per current laws, married couples cannot own more than one HDB dwelling. So, they wanted to sell both of their flats and buy a new one together.
"We put both houses on the market and ended up selling my house first," recalls Suhardi. "After that, we wanted to wait for Norlina's house to sell and buy a five-room flat."
Want to buy a four-room HDB flat in Singapore? Check out PropertyGuru to find thousands of homes for sale in Singapore.
While they waited, they both browsed PropertyGuru to look for potential new homes that would fit the needs of their new combined family.
However, the property market wasn't doing well; Suhardi had sold his home at a loss of more than 100k, and there weren't many interested buyers for Norlina's home. Time wasn't on their side either, as Norlina had quit her full-time job and her CPF that was being used to pay for it was running out. The couple decided not to sell the house after all, and Suhardi took over financing it.
Though finances were tight, the couple managed to obtain a full redemption financial release from HDB to pay off Norlina's house. "After calculating the outstanding loan amount, we split it into two to manage the balance payment of my house," explains Norlina.
Read: 4 Reasons You Should Not Use Your CPF To Pay For Your House
Fortunately, moving the family to Norlina's home benefited both parties. For one, Suhardi is happy that Norlina's flat is much bigger than his previous home.
"It was tiring having to maintain both houses and go back and forth after work," adds Norlina. "During that period, my youngest son Hadziq was alone at home most of the time too, as my two older boys had married and moved out."
With the new living arrangement however, Hadziq has companionship at home. He shares a room with his stepbrother Husainy, Suhardi's eldest son.
"Both Hadziq and Husainy get along very well and they are both 23 years old," says Norlina. "Incidentally, they were born just two months apart!"
She also loves the location of her home, with it being surrounded by plenty of food options and near Causeway Point mall and Johor Bahru.
Having everyone settled in their current abode, there are no plans to move out. Instead, they are looking to renovate and spruce up the home, particularly the kitchen, toilets and wardrobes. They also plan to install air-conditioning.
"When the rest of the kids grow up, they will get married and have their own homes," says Norlina. "Then it will be just the two of us, and we'll have more than enough space!"
Read stories of real people and their home journeys. Or find a home on PropertyGuru now.


Research round three - How are self driving cars programed?
Inquiry Question: Are self driving cars worth all the ethical problems they cause?
This round, I will be looking at: How are the cars programed to solve the moral problems?
So the firs thing I looked at was how they are solving the "who should the car protect/kill" debate. The car has to be programed to kill someone, either the passenger or the pedestrian. Technically, the pedestrians generally have the right of way, so cars will be programed to protect them instead of the passenger. Some people may not feel safe inside a car programed to kill them, so they might try to alter the car's programing. But how do we stop this? An easy answer would be to design the car to only accept software that has been approved by the Ministry of Transportation. This is a sort of digital lock, and it is illegal to teach other people how to over ride the locks.
Unfortunately, a locked device can still be hacked into. Digital locks don't make it impossible to reprogram the devices, they just make sure that if you want to reconfigure your device, you have to do it without the use of a business or a product. A good example of this is with mobile phones. Before the law was clarified in 2002, you could unlock your phone by simply downloading a software or asking someone experienced to help you.
So my first source states explains why the self driving cars will be programed to protect pedestrians, but my second source said that most cars, such as Mercedes cars, will be programed to protect the passenger. I decided to do some digging and see which page is more reliable. I noticed that my first website had no sources, and is written by someone unqualified in this subject. The second page was still written by a journalist, but they referenced several interviews with qualified people, and they referenced other sources. I personally think that my second source is more reliable, but the first one is still full of useful information. This is the perfect example of how confused we are about self driving cars. I think that everyone should be better informed before we actually start using these cars. There are tons of articles like these two that contradict each other.
We need to clear the air, because self driving cars are going to be on the streets even faster than I originally thought. It is estimated that in two years, there will be 10 million self-driving cars on the road. Children born today won't even need to get a driver's license! In only 11 years, there will only be 44 million passenger driven vehicles on American roads!
Wow good job! Your topic is really intriguing, and I've always been fascinated with self-driving vehicles. I've always had this curiosity in what a self-driving car/its program would do in a scenario that they can't avoid. Would they "favor" the safety of the pedestrian or occupants? Something that I found you did a really good job while researching is the effort you demonstrated in finding the variety of good reputable resources and your journey. I agree with you: there are, in fact, tons of articles that contradict one another on the internet. It is crucial in coming into the conclusion which pieces of information are "legit". I read in an article, similar to the first website I've attached below, that mentioned how one of the biggest fears of having self-driving cars is that other road users might drive erratically in order the program on the driverless cars into submission. I find these potential dangers from other road users to be really interesting, and for you to look into them if you didn't decide to already, would be exciting! You can also investigate other self-driving vehicle developments. Even so, next time, for your future rounds of research, I suggest you link the articles you found because I believe it would be beneficial for readers to trace back your information back to the source if they are intrigued to learn more! Nonetheless, you did a formidable job!
Hey @Jasmine Paduraru (Charles Best)!
Now, your research is definitely helping me do that! I know it is funny, I will read this for minutes than spend seconds googling it.
It has been interesting how the technology has been advancing. In the next 20 years it'll definitely really impact how the world was in the 2000s than 2020s. There are videos on Tesla cars using its auto avoid crash system which of course, working progress but a step towards a perfected model.
Metamorphosis coming up? Short is fine! There are always modifications needed to be made. Great job this research cycle!
Great round of research! I really like how you formatted it and how detailed it was. It's unfortunate that many locks can still be hacked into, it makes me wonder if in the future, people could develop a lock that has absolutely no way of being broken into, if that's even possible. Overall, great job this cycle, reading your research has been really intriguing to me.
Good luck on your metamorphosis!
This seems like a question with no definitive question considering both sides have their points, and I have to say I absolutely love it. I have to say though that it would be damn near impossible to actually make it impossible for no one to alter the code or program for the car even with the so-called digital lock; the reason I say this is because we can already do it with devices and even if we didn't people would more then likely come up with a rooted version of the software that can be put in without the lock. I think a much better way of doing it would be to make it illegal to alter with the code, as any errors a person makes could lead to very costly errors. As to their being different answers, well different companies have different priorities, standards, and creators, but more so there isn't a definitive answer because currently there aren't really any laws regulating it or an answer to the question that would make a company look good. I'm curious to know more about this subject and how it goes in the future as I have the feeling that it will greatly impact us in the future. I can't wait to see what conclusion you come to.
hey Jasmine,i liked your post.


text:
\section{INTRODUCTION}\label{header-n1}
When DQN is announced by DeepMind in 2015, the whole world is surprised by its simplicity and capability to solve all kinds of Atari games \cite{DQN}. The DeepMind system uses a deep convolutional neural network to represent the Q function, and experience replay buffer is introduced to improve training stability by removing correlations in the observation sequence and smoothing changes in the data distribution. Since then, many improvements have been made to DQN. Besides the algorithmical modification, distributed training frameworks are also developed so as to solve large scale environments.
Double DQN \cite{DDQN} addresses the overestimation problem of Q-function by using two Q networks to decouple action selection and Q-value evaluation. Prioritized experience replay \cite{PER} improves data efficiency by replaying more with transition tuples that is less fitted. Ape-X DQN proposes a distributed architecture for Q-learning at scale by decoupling acting from learning \cite{APEX}. Recurrent Replay Distributed DQN (R2D2) investigates the training of RNN-based RL agents from distributed prioritized experience replay  \cite{R2D2}.

In spite of the great improvements made on DQN, the inherent problems of DQN remain.
First of all, it can suffer from the sub-optimum problem. In the algorithm design, a decaying exploration factor needs to be artificially added and tuned in order to let learning happen. It usually results in a solution doing well in the short term, but under-performing in the long run.  Secondly, like many other reinforcement learning methods, DQN tends to have low sample-efficiency. Take the standard baseline environments Atari57 for example, billions of interaction steps are needed to solve each one of the environments\cite{R2D2}\cite{APEX}. Last but not least, DQN is very sensitive with respect to its hyper-parameters. Parameter tuning can be time-consuming and harm reproducibility. Due to these shortcomings, large scale reinforcement learning projects like DOTA2 and StarCraftII \cite{Dota2}\cite{AlphaStar} use policy gradient based algorithms instead of algorithms based on Q-learning.

In reinforcement learning, explore-exploit trade-off is an eternal problem. In order to balance the explore-exploit trade-off, we introduce entropy regularization into DQN and propose Soft Q Network (SQN) in this work. Entropy regularization is an important idea in reinforcement learning \cite{SQL}. In addition to the environment reward, entropy regularization brings in an entropy reward which encourages exploration. SQN no longer needs the intractable exploration factor, instead the exploration of SQN is controlled by an adaptive parameter $\alpha$ called temperature.

Q-value based deep reinforcement learning algorithms apply approximate dynamic programming (ADP) with function approximation. ADP methods rely on previous Q-functions to generate targets for training the current Q-function. As pointed out in \cite{DisCor}, because the target values are computed by applying a Bellman backup on the previous Q-function, $\Hat{Q}$ (target value), rather than the optimal $Q^{*}$, errors in $\Hat{Q}$ at the next states can result in incorrect Q-value targets at the current state. No matter how often the current transition is observed, or how accurately Bellman errors are minimized, the error in the Q-value with respect to the optimal Q-function $|Q {-} Q^{*}|$ at this state is not reduced. We show that "the corrective feedback problem" can be eliminated theoretically for Soft Q-Learning (SQL) with a small modification on the standard SQL algorithms. We call the new SQL algorithm variant Soft Q-Learning with Corrective Feedback (SQL-CF), which includes Soft Q Network with Corrective Feedback (SQN-CF) and Soft Actor-Critic with Corrective Feedback (SAC-CF) for the discrete and continuous cases, respectively.

In this work, we also propose the Q On-Polcy (QOP) algorithm, a distributed variant of SQN-CF with n-step backup. It is worth noting that SQN is an off-policy algorithm while QOP is an on-policy algorithm. It can be proven that SQL equals Soft Policy Gradient (SPG) \cite{schulman2017equivalence} \cite{SPG}. Since policy gradient is an on-policy algorithm, we speculate that SQL can also been trained in an on-policy fashion. In order to verify this extrapolation, we experiment with the QOP algorithm on an self-play reinforcement learning environment called Google Research Football (GRF), a new reinforcement learning environment where agents are trained to play football \cite{GRF}. We identify the strong correlation between the reuse ratio of sampling data and training speed, which indicates the on-policy nature of the QOP algorithm. Our experiment shows the training stability and efficiency of SQN and QOP, which can generate strong agents with diverse attacking and defending strategies.

\section{BACKGROUND}\label{header-n2}

\subsection{MDP}\label{header-n21}

The key problem of RL is searching for a policy that maximizes the accumulate future rewards
in a Markov Decision Process \((\text{MDP})\) defined by
the tuple \((\mathcal{S,A,P,R})\) \cite{planet}.

\begin{itemize}
\item
  \(\mathcal {S}\) represents a set of states
\item
  \(\mathcal {A}\) represents a set of actions,
\item
  \(\mathcal {P:S\times A\to P(S)}\) stands for the transition function which maps
  state-actions to probability distributions over next states
  \(\text{P}(s^{\prime}|s,a)\)
\item
  \(\mathcal {R}:\mathcal{S\times A }\to \mathbb{R} \) corresponds to the
  reward function, with \(r_t=R(s_t,a_t)\)
\end{itemize}

Within this framework, the agent acts in the environment according to a policy $a  \sim \pi(\cdot|s)$.
the environment changes to a new state following $s^{\prime} \sim \mathcal{P}(\cdot|s, a)$ and provides a reward $r$. The agent iterates its policy based on the information gathered via interacting with the environment to obtain an optimal policy:
\begin{equation}
    \pi^* = \arg \underset{\pi}\max \E{\tau \sim \pi}{ \sum_{t=0}^{\infty} \gamma^t  r(s_t, a_t) }
\end{equation}{}

\subsection{Maximum Entropy Reinforcement Learning}\label{header-n22}

The maximum entropy reinforcement learning generalizes the standard objective with an entropy term, such that the optimal policy additionally aims to maximize its entropy at each visited state \cite{SQL}. The temperature parameter $\alpha$ that determines the relative importance of the entropy term versus the reward is account for the explore-exploit balance.
\begin{equation}
    \pi^* = \arg \underset{\pi}\max\E{\tau \sim \pi}{ \sum_{t=0}^{\infty} \gamma^t \bigg( r(s_t, a_t) + \alpha H\left(\pi(\cdot|s_{t+1})\right) \bigg)}.
\end{equation}{}

The temperature parameter $\alpha$ can be tuned to encourage exploration. When $\alpha$ is large, the policy will be more stochastic, on the contrary, when $\alpha$ is small, the policy will become more deterministic. In the limit $\alpha \rightarrow 0$, We can recover exactly the standard reinforcement learning. The maximum entropy objective has a number of conceptual and practical advantages. The policy is incentivized to explore more widely, capturing multiple modes of near-optimal behavior while giving up on unpromising avenues. Unlike the standard reinforcement learning, in problem settings where multiple actions seem equally attractive, the policy will commit equal probability mass to those actions instead of collapsing into one action randomly.

In the entropy-regularized framework, \(Q^{\pi}\) should be modified to include the entropy term:
\begin{align}
    Q^{\pi}(s,a) &= \E{\tau \sim \pi}{ \left. \sum_{t=0}^{\infty} \gamma^t \bigg( r(s_t, a_t) + \alpha  H\left(\pi(\cdot|s_{t+1})\right) \bigg) \right| s_0 = s, a_0 = a}.
    \label{eq:q}
\end{align}
We can define the value function \(V^{\pi}\) as:
\begin{align}
    V^{\pi}(s) &= \E{a \sim \pi}{Q^{\pi}(s,a)+ \alpha  H\left(\pi(\cdot|s)\right)},
\end{align}
and rewrite the modified Bellman backup of \(Q^{\pi}\) as:
\begin{align}
    Q^{\pi}(s,a) &= r(s,a)  + \gamma \E{s' \sim P}{V^{\pi}(s')}. \label{eq:backup}
\end{align}

\section{METHOD}\label{header-n3}

\subsection{Soft Q Network}
We will begin by introducing the soft policy evaluation step of soft policy iteration. The soft Q-value for a fixed policy with  the maximum entropy objective can be computed iteratively, following the Bellman backup according to Eq.(\ref{eq:backup}).
With the modified Bellman backup operator $\mathcal{T}^\pi_\theta$, we have:
\begin{align}
\mathcal{T}^\pi_\theta Q({\state_t}, {\action_t}) \triangleq  r({\state_t}, {\action_t}) + \gamma \E{\state_{t+1} \sim \density_\state}{V(\state_{t+1})}, \label{eq:soft_bellman_backup_op}
\end{align}
where
\begin{align}
V({\state_t}) = \E{{\action_t}\sim\pi_\theta}{Q({\state_t}, {\action_t}) - \alpha\log\pi_\theta({\action_t}|{\state_t})}
\label{eq:soft_value_function}
\end{align}
is the soft state value function. We can obtain the soft Q-function for any policy $\pi_\theta$ by repeatedly applying $Q^{k+1} = \mathcal{T}^\pi_\theta Q^k$ given an arbitrary starting $Q^0$. The sequence $Q^k$ will converge to the soft Q-function of $\pi_\theta$ as $k\rightarrow \infty$.

For discrete reinforcement learning, it is convenient to define the policy from the learned Q function without using an additional policy network, such as DQN. Different from DQN, Soft Q Network uses a softmax policy instead of a hard max policy. We define the policy as:
\begin{align}
\pi_\theta({\,\cdot\,}|{\state_t}) &= \text{softmax}  \left(  Q ({\state_t}, {\,\cdot\,}) / \alpha \right) \notag\\
& = {\exp\left(Q ({\state_t},{\,\cdot\,}) / \alpha  - \log Z ({\state_t})\right)}
\label{eq:softmax policy}
\end{align}
where $Z ({\state_t})$ is the partition function for normalization:
\begin{align}
Z ({\state_t}) = \sum_{i=1}^n \exp\left(Q ({\state_t}, a_t^i)/ \alpha \right)
\label{eq:z}
\end{align}
It can be proven that Eq.(\ref{eq:softmax policy}) is a policy improvement, i.e. the soft policy improvement.
Let $Q^{\policy_\mathrm{old}}$ and $V^{\policy_\mathrm{old}}$ be the corresponding soft state-action value and soft state value of policy ${\policy_\mathrm{old}}$, and let ${\policy_\mathrm{new}}$ be defined according to Eq.(\ref{eq:softmax policy}) as:
\begin{align}
{\policy_\mathrm{new}}({\,\cdot\,}|{\state_t}) &= {\exp\left(Q^{\policy_\mathrm{old}} ({\state_t},{\,\cdot\,}) / \alpha  - \log Z^{\policy_\mathrm{old}} ({\state_t})\right)}. \label{eq:pi_new}
\end{align}
By using the non-negative property of Kullback–Leibler divergence
\begin{align}
\mathrm{D_{KL}}{{\policy_\mathrm{old}}({\,\cdot\,}|{\state_t})}{{\policy_\mathrm{new}}({\,\cdot\,}|{\state_t})} \geq \mathrm{D_{KL}}{{\policy_\mathrm{new}}({\,\cdot\,}|{\state_t})}{{\policy_\mathrm{new}}({\,\cdot\,}|{\state_t})} =0
\end{align}
and substituting ${\policy_\mathrm{new}}$ on the right-hand side of Kullback–Leibler divergence with Eq.(\ref{eq:pi_new}), the inequality can be expanded as:
\begin{align}
\resizebox{1\textwidth}{!}{$
\E{{\action_t}\sim{\policy_\mathrm{old}}}{\log {\policy_\mathrm{old}}({\action_t}|{\state_t}) - Q^{\policy_\mathrm{old}}({\state_t},{\action_t})/\alpha + \log Z^{\policy_\mathrm{old}}({\state_t})}
\geq  \E{{\action_t}\sim{\policy_\mathrm{new}}}{\log {\policy_\mathrm{new}}({\action_t}|{\state_t}) - Q^{\policy_\mathrm{old}}({\state_t}, {\action_t})/\alpha + \log Z^{\policy_\mathrm{old}}({\state_t})}
$}.
\end{align}
Since partition function $Z^{\policy_\mathrm{old}}$ depends only on the state, we have:
\begin{align}
 V^{\policy_\mathrm{old}}({\state_t}) \leq \E{{\action_t}\sim{\policy_\mathrm{new}}}{Q^{\policy_\mathrm{old}}({\state_t}, {\action_t}) - \alpha \log {\policy_\mathrm{new}}({\action_t}|{\state_t})} .
\label{eq:soft_value_bound}
\end{align}
By applying the soft Bellman equation iteratively, we can confirm the soft policy improvement \cite{SQL} \cite{SAC1}.
\begin{align}
Q^{\policy_\mathrm{old}}({\state_t}, {\action_t}) &= r({\state_t}, {\action_t}) + \gamma\E{\state_{t+1}\sim\density_\state}{V^{\policy_\mathrm{old}}(\state_{t+1})}  \label{eq:bell1}\\
&\leq r({\state_t}, {\action_t}) + \gamma\E{\state_{t+1}\sim\density_\state}{\E{{\action_{t+1}}\sim{\policy_\mathrm{new}}}{Q^{\policy_\mathrm{old}}(\state_{t+1}, {\action_{t+1}}) - \alpha \log {\policy_\mathrm{new}}({\action_{t+1}}|\state_{t+1})}}  \label{eq:bell2} \\
&\ \  \vdots\notag\\
& \leq Q^{\policy_\mathrm{new}}({\state_t}, {\action_t}). \label{eq:bell3}
\end{align}

\begin{figure}
    \centering
    \includegraphics[width=0.795\textwidth]{image/sqn.png}
    \caption{The SQN algorithm. There are two Q function networks in the framework. Q function networks are updated with Q value backups and the policy is defined as the softmax of the learned Q function.}
    \label{fig:sqn}
\end{figure}

Combining the soft policy evaluation and the soft policy improvement, we have the soft policy iteration of SQN. The double Q-learning trick is applied in SQN to mitigate over-estimation of the Q functions \cite{TD3}. In addition to the double Q networks, two more target Q networks are introduced to stabilize training. The two target Q networks are updated by polyak averaging the parameters of the two main Q networks over the course of training. The pseudo-code of SQN is presented in Fig.\ref{fig:sqn}, where we propose three different schemes for the backup of soft Q functions.

\subsection{SQN with Corrective Feedback}

In the policy evaluation step, Bellman backup relies on previous Q-functions to generate targets for training the current Q-function. As shown in \cite{DisCor}, deep Q learning can have "the corrective feedback problem". If the target values are computed by applying a Bellman backup on the optimal $Q^{*}$, the Q-function is updated with corrective feedback. But in practice the Q-function is updated with non-corrective Q-value targets, which can harm training efficiency.

Here we show that SQL can be modified to enjoy the corrective feedback. Instead of viewing the Bellman backup equation as policy evaluation, the soft Bellman backup equation can be transformed to be policy improvement in the form of Q value. Let ${\policy_\mathrm{new}}$ be defined as Eq.(\ref{eq:pi_new}):
\begin{align}
{\policy_\mathrm{new}}({\,\cdot\,}|{\state_t}) &= {\exp\left(Q^{\policy_\mathrm{old}} ({\state_t},{\,\cdot\,}) / \alpha  - \log Z^{\policy_\mathrm{old}} ({\state_t})\right)}, \label{eq:pi_new1}
\end{align}
and define a new soft Bellman backup of Q function:
\begin{align}
Q({\state_t}, {\action_t}) = r({\state_t}, {\action_t}) + \gamma\E{\state_{t+1}\sim\density_\state}{\E{{\action_{t+1}}\sim{\policy_\mathrm{new}}}{Q^{\policy_\mathrm{old}}(\state_{t+1}, {\action_{t+1}}) - \alpha \log {\policy_\mathrm{new}}({\action_{t+1}}|\state_{t+1})}}, \label{eq:backup1}
\end{align}
from Eq.(\ref{eq:bell2}), we have:
\begin{align}
Q({\state_t}, {\action_t}) \geq  Q^{\policy_\mathrm{old}}({\state_t}, {\action_t}). \label{eq:backup2}
\end{align}
Substituting $Q^{\policy_\mathrm{old}}({\state_t}, {\action_t})$ in Eq.(\ref{eq:pi_new1}) with $Q({\state_t}, {\action_t})$ in Eq.(\ref{eq:backup1}), i.e. $\pi_\theta({\,\cdot\,}|{\state_t}) = {\exp\left(Q ({\state_t},{\,\cdot\,}) / \alpha  - \log Z ({\state_t})\right)}$ and applying the above inequality and the soft policy improvement inequality, we obtain:
\begin{align}
Q^\pi_\theta({\state_t}, {\action_t}) \geq  Q({\state_t}, {\action_t}) \geq Q^{\policy_\mathrm{old}}({\state_t}, {\action_t}). \label{eq:policyimprove}
\end{align}
Repeating the two steps of Eq.(\ref{eq:pi_new1},\ref{eq:backup1}), we obtain the policy improvement in the form of $Q$ value according to Eq.(\ref{eq:policyimprove}). In other words, every Bellman backup step of SQN-CF is guaranteed to be a policy improvement and has corrective feedback.

The new soft Bellman backup in Eq.(\ref{eq:backup1}) can be further reduced:
\begin{align}
Q({\state_t}, {\action_t})
&= r({\state_t}, {\action_t}) + \gamma\E{\state_{t+1}\sim\density_\state}{\E{{\action_{t+1}}\sim{\policy_\mathrm{new}}}{Q^{\policy_\mathrm{old}}(\state_{t+1}, {\action_{t+1}}) - \alpha \log {\policy_\mathrm{new}}({\action_{t+1}}|\state_{t+1})}}  \notag \\
&= r({\state_t}, {\action_t}) + \gamma\E{\state_{t+1}\sim\density_\state}{ \alpha \log \sum_{i=1}^n \exp\left( Q^{\policy_\mathrm{old}} (\state_{t+1},a_{t+1}^i) / \alpha \right) }  \notag \\
&= r({\state_t}, {\action_t}) + \gamma\E{\state_{t+1}\sim\density_\state}{ \alpha \log Z^{\policy_\mathrm{old}}(\state_{t+1}) } \notag \\
&= r({\state_t}, {\action_t}) + \gamma\E{\state_{t+1}\sim\density_\state}{ V^{\policy_\mathrm{old}}(\state_{t+1}) } .\label{eq:backup1a}
\end{align}
With the new soft Bellman backup, we can construct the SQN-CF algorithm. The pseudo-code of SQN-CF is presented in Fig.\ref{fig:sqn-cf2}. There are two main differences between SQN-CF and SQN, one is that the policy is defined according to the target Q function, the other is different formula of the soft Bellman backup. The deduction of SQN-CF is general. It holds for SQL and we have SQL-CF. As for the continuous case of SQL-CF, SAC-CF can be derived.

\begin{figure}
    \centering
    \includegraphics[width=0.795\textwidth]{image/sqn-cf2.png}
    \caption{The SQN-CF algorithm. }
    \label{fig:sqn-cf2}
\end{figure}

\subsection{Q On Policy}
As pointed out in \cite{schulman2017equivalence}, SQL equals Soft Policy Gradient (SPG). With SQL-CF, the proof of the equivalence of SQL and SPG is straightforward. We can obtain the equality $Q_\theta({\state_t}, {\action_t}) = V_\theta({\state_t}) + \alpha \log \pi_\theta_\theta({\action_t}|{\state_t}) $ with Eq.(\ref{eq:pi_new1}). By substituting $Q_\theta({\state_t}, {\action_t})$ with $V_\theta({\state_t})$ and $\pi_\theta_\theta({\action_t}|{\state_t})$, we have:
\begin{align}
&\bigtriangledown_\theta \E{{\state_t},{\action_t}\sim\pi_\theta} {\frac{1}{2}||Q_\theta({\state_t}, {\action_t})-\Hat{Q}_t||^2}|_{\pi_\theta=\pi_\theta_\theta} \notag\\
&= \E{{\state_t},{\action_t}\sim\pi_\theta} {(\bigtriangledown_\theta Q_\theta({\state_t}, {\action_t})) (Q_\theta({\state_t}, {\action_t})-\Hat{Q}_t) }|_{\pi_\theta=\pi_\theta_\theta} \notag \\
&= \E{{\state_t},{\action_t}\sim\pi_\theta} {(\bigtriangledown_\theta V_\theta({\state_t}) + \alpha \bigtriangledown_\theta \log \pi_\theta_\theta({\action_t}|{\state_t})) (V_\theta({\state_t}) + \alpha \log \pi_\theta_\theta({\action_t}|{\state_t})-\Hat{Q}_t) }|_{\pi_\theta=\pi_\theta_\theta} \notag \\
&= \E{{\state_t},{\action_t}\sim\pi_\theta} {\bigtriangledown_\theta V_\theta({\state_t})(V_\theta({\state_t}) -\Hat{Q}_t)  + \alpha \bigtriangledown_\theta \log \pi_\theta_\theta({\action_t}|{\state_t}) (V_\theta({\state_t}) + \alpha \log \pi_\theta_\theta({\action_t}|{\state_t})-\Hat{Q}_t) }|_{\pi_\theta=\pi_\theta_\theta} \notag \\
&= \E{{\state_t},{\action_t}\sim\pi_\theta} {\bigtriangledown_\theta \frac{1}{2}||V_\theta({\state_t})-\Hat{Q}_t||^2
- \alpha \bigtriangledown_\theta \log \pi_\theta_\theta({\action_t}|{\state_t}) ( \Hat{Q}_t - V_\theta({\state_t}) - \alpha \log \pi_\theta_\theta({\action_t}|{\state_t})) }
|_{\pi_\theta=\pi_\theta_\theta}
\end{align}
The above equation is exactly the objective gradient of SPG \cite{SPG}. The first term is for Value estimation and the second term is for policy gradient.

In Eq.(\ref{eq:backup1}), the target is defined by one-step backup. We can extend it into n-step backup and verify that Eq.(\ref{eq:backup2}) still holds, that is to say, we have the n-step SQL-CF algorithm, i.e. the QOP alogrithm.
The equivalence of SQL and SPG indicates the on-policy nature of SQL. From SQL-CF, we know that the soft Bellman backup of SQL-CF provide a policy improvement in the form of $Q$ value ( similar to the policy improvement in the PG algorithm ), which underlies the on-policy nature of SQL.

\section{EXPERIMENT}\label{header-n4}

In the last section, we propose three algothrims: SQN, SQN-CF, QOP. SQN-CF is based on SQN and QOP is based on SQN-CF. All the three algorithm can be used to solve RL environments. The code of the three algorithms can be found in \url{https://github.com/createamind/DRL}. SQN is to DQN what SAC is to DDPG. DQN and DDPG are the discrete version and the continuous version of Q learning, respective. Similarly SQN and SAC are the discrete version and the continuous version of soft Q learning, respective. So it is reasonable to assume SQN can have all the advantages of SAC  comparing with DQN and DDPG.

In this work, we are particularly interested in the on-policy Q learning, which is seldom studied in the RL community before. In this section, we focus on QOP and show the effectiveness and efficiency of the QOP alogrithm. We apply QOP to train GRF agent. GRF is a novel open-source reinforcement learning environment where agents learn to play football \cite{GRF}. The project can be found in \url{https://github.com/google-research/football}. There are several representations available for the observation state. We choose the simple115 representation, i.e. 115 floats to represent one frame state of observation. The action space is composed of 19 discrete actions. The reward is sparse, denoting the score of a team.

We build a training framework based on a distributed framework called Ray \cite{Ray}. The project of Ray can be found in \url{https://github.com/ray-project/ray}.  The structure of our training framework is prsented in Fig. \ref{fig:ddrlframework}. The main components of our framework are two remote actors, i.e. ReplayBuffer, ParameterServer, and three romote functions, i.e. train worker, roll-out worker, test worker. We also introduce a Cache for train worker, in order to reduce the waiting time of the trainer. Their functions are presented in Fig. \ref{fig:ddrl5}.  The code of our training framework can be found in \url{https://github.com/createamind/Distributed-DRL}.

Our agent is trained by 'self-play'. Training data is generated by current agent playing with diverse opponents. In training 80\% of the games are played against a target opponent, and 20\% play against past versions which are stored in a weights pool.
We did not adopt the pure self-play training because we thought that the external entropy reward breaks the zero-sum rule of self-play and can have negative influence in the training process.
The target opponent is updated when current agent can win the target opponent with winning rate of 60\%. The Q network is represented as a (600, 800, 600) MLP with ReLU activation.
The QOP algorithm can be seen as n-step SQN-CF. Generalized Advantage Estimation (GAE) is the generalized version of the normal advantage estimation. GAE backup can reduce to n-step backup when $\lambda = 1$. In the experiment, we use n-step backup for the sake of simplicity but we think GAE backup is a better option. GAE backup is more stable and efficient than n-step backup.
In the training process, we find that the reuse ratio of sampling data can have great impact on the training speed. We tune the reuse ratio by controlling the network update frequency. The smaller reuse ratio we take, the higher training speed we get, even when the reuse ratio is less than 1. It indicates the on-policy training nature of QOP. The QOP algorithm can generate strong GRF agent with diverse attack and counter-acttack strategies, and as shown in Fig. \ref{fig:leaderboard} our agent Cuju win the first place of the GRF leaderboard in April of 2020.

\begin{figure}
    \centering
    \includegraphics[width=0.759\textwidth]{image/fb_standing.png}
    \caption{QOP can generate strong GRF agent, which stands in the first place of the GRF leaderboard.}
    \label{fig:leaderboard}
\end{figure}

\begin{figure}
    \centering
    \includegraphics[width=0.759\textwidth]{image/ddrlframework.jpg}
    \caption{The training framework.}
    \label{fig:ddrlframework}
\end{figure}

\begin{figure}
    \centering
    \includegraphics[width=0.8\textwidth]{image/ddrl5.png}
    \caption{The APIs of our training framework.}
    \label{fig:ddrl5}
\end{figure}

\section{CONCLUSION}\label{header-n5}

Deep Q Network is a simple algorithm,  yet it is surprisingly successful. Since its announcement many improvements have been made to enhance its performance, yet the inherent problem of DQN, i.e. the exploit-explore balance, remains. In this work, we investigate the effect of entropy regularization on DQN and propose SQN. SQN is the soft version of DQN, just like SAC (SDDPG) is the soft version of DDPG.  All the improvements on DQN, such as IMPALA\cite{espeholt2018impala}, APEX\cite{APEX}, R2D2\cite{R2D2} etc., can be equally effective on SQN.
For deep Q network, an exploratory factor like $\epsilon$-greed needs to be artificially added and tuned for training. While SQN can automatically balance exploit-exploit through the temperature $\alpha$. Compared with DQN, SQN handles exploit-exploit trade-off more elegantly and the performance is much better.
We show that SQL can have the corrective feedback effect and propose SQL-CF. The soft backup of SQL-CF can be seen as a policy improvement in the form of Q, and it underlies the equivalence of SQL and Soft Policy Gradient (SPG). With the insight of the on-plicy nature of SQL, we propose an on-policy version of deep Q learning algorithm, i.e. Q On-Policy (QOP). Different from n-step DQN, QOP has rigorous policy improvement guarantee. We experiment with QOP on GRF and the QOP algorithm shows great stability and efficiency in training.

\bibliographystyle{apalike}

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Printed from https://ideas.repec.org/a/kap/openec/v28y2017i5d10.1007_s11079-017-9460-1.html
The macroeconomic effects of the ECB's evolving QE programme: a model-based analysis
Romanos Priftis
(European Commission)
Lukas Vogel
This paper analyses the macroeconomic effects of the European Central Bank's asset purchase programme in its initial version and subsequent modifications under the lens of a dynamic macroeconomic model, which includes assets of different types and maturity, and explicitly introduces asset purchases of long-term bonds (held by euro area and non-euro area residents) by the central bank. With imperfect substitutability between asset classes, portfolio rebalancing in the context of quantitative easing (QE) affects bond yields, stock prices, the exchange rate and the private sector's saving decision. QE as announced in January 2015 generates 0.4% effective euro depreciation and raises real GDP in the euro area by 0.2% and prices by 0.3% by 2017 in the model. The subsequent extensions of the QE programme (extension in time and increase in volume) more than double the medium-term output and inflation effects according to the simulations.
Romanos Priftis & Lukas Vogel, 2017. "The macroeconomic effects of the ECB's evolving QE programme: a model-based analysis," Open Economies Review, Springer, vol. 28(5), pages 823-845, November.
Handle: RePEc:kap:openec:v:28:y:2017:i:5:d:10.1007_s11079-017-9460-1
File URL: http://link.springer.com/10.1007/s11079-017-9460-1
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File URL: https://libkey.io/10.1007/s11079-017-9460-1?utm_source=ideas
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Daisuke Ikeda & Shangshang Li & Sophocles Mavroeidis & Francesco Zanetti, 2021. "Testing the effectiveness of unconventional monetary policy in Japan and the United States," Economics Series Working Papers 961, University of Oxford, Department of Economics.
Daisuke Ikeda & Shangshang Li & Sophocles Mavroeidis & Francesco Zanetti, 2020. "Testing the effectiveness of unconventional monetary policy in Japan and the United States," Papers 2012.15158, arXiv.org, revised Oct 2022.
Serdar Kabaca, 2016. "Quantitative Easing in a Small Open Economy: An International Portfolio Balancing Approach," Staff Working Papers 16-55, Bank of Canada.
Haroon Mumtaz & Konstantinos Theodoridis, 2017. "The Federal Reserve's implicit inflation target and Macroeconomic dynamics. A SVAR analysis," Working Papers 173173908, Lancaster University Management School, Economics Department.
Haroon Mumtaz & Konstantinos Theodoridis, 2017. "The Federal Reserve's implicit inflation target and Macroeconomic dynamics. A SVAR analysis," Working Papers 820, Queen Mary University of London, School of Economics and Finance.
Mumtaz, Haroon & Theodoridis, Konstantinos, 2018. "The Federal Reserve's implicit inflation target and Macroeconomic dynamics. A SVAR analysis," Cardiff Economics Working Papers E2018/1, Cardiff University, Cardiff Business School, Economics Section.
Sami Alpanda & Serdar Kabaca, 2020. "International Spillovers of Large-Scale Asset Purchases," Journal of the European Economic Association, European Economic Association, vol. 18(1), pages 342-391.
Sami Alpanda & Serdar Kabaca, 2015. "International Spillovers of Large-Scale Asset Purchases," Staff Working Papers 15-2, Bank of Canada.
Sami Alpanda & Uluc Aysun & Serdar Kabaca, 2022. "International Portfolio Rebalancing and Fiscal Policy Spillovers," Working Papers 2022-01, University of Central Florida, Department of Economics.
Ferre De Graeve & Konstantinos Theodoridis, 2016. "Forward Guidance, Quantitative Easing, or both?," Working Paper Research 305, National Bank of Belgium.
M. Falagiarda & M. Marzo, 2012. "A DSGE model with Endogenous Term Structure," Working Papers wp830, Dipartimento Scienze Economiche, Universita' di Bologna.
Andreas Tischbirek, 2016. "Unconventional Monetary Policy in a Currency Union with Segmentation in the Market for Government Debt," Cahiers de Recherches Economiques du Département d'économie 16.16, Université de Lausanne, Faculté des HEC, Département d'économie.
Michael Chin & Thomai Filippeli & Konstantinos Theodoridis, 2015. "Cross-Country Co-movement in Long-Term Interest Rates: A DSGE Approach," Working Papers 753, Queen Mary University of London, School of Economics and Finance.
Chin, Michael & Filippeli, Thomai & Theodoridis, Konstantinos, 2015. "Cross-country co-movement in long-term interest rates: a DSGE approach," Bank of England working papers 530, Bank of England.
Tischbirek, Andreas, 2018. "Large-scale bond purchases in a currency union with segmentation in the market for government debt," Journal of Economic Dynamics and Control, Elsevier, vol. 95(C), pages 37-69.
Gregor Boehl & Gavin Goy & Felix Strobel, 2020. "A Structural Investigation of Quantitative Easing," Working Papers 691, DNB.
Kühl, Michael, 2014. "Mitigating financial stress in a bank-financed economy: Equity injections into banks or purchases of assets?," Discussion Papers 19/2014, Deutsche Bundesbank.
Darracq Pariès, Matthieu & Notarpietro, Alessandro & Kilponen, Juha & Papadopoulou, Niki & Zimic, Srečko & Aldama, Pierre & Langenus, Geert & Alvarez, Luis Julian & Lemoine, Matthieu & Angelini, Elena, 2021. "Review of macroeconomic modelling in the Eurosystem: current practices and scope for improvement," Occasional Paper Series 267, European Central Bank.
Michael Kühl, 2018. "The Effects of Government Bond Purchases on Leverage Constraints of Banks and Non-Financial Firms," International Journal of Central Banking, International Journal of Central Banking, vol. 14(4), pages 93-161, September.
Kühl, Michael, 2016. "The effects of government bond purchases on leverage constraints of banks and non-financial firms," Discussion Papers 38/2016, Deutsche Bundesbank.
Tischer, Johannes, 2018. "Quantitative easing, portfolio rebalancing and credit growth: Micro evidence from Germany," Discussion Papers 20/2018, Deutsche Bundesbank.
Quantitative easing; Portfolio adjustment; Asset prices; Real sector;
E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
F41 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Open Economy Macroeconomics
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Been diving in Taiwan? Please Vote!
Taiwan is a small country that straddles the Tropic of Cancer and measures only 450 kilometres in length and 200 kilometres in width. Taiwan is an undiscovered gem amongst the well-known and ever-popular Asian diving destinations such as Indonesia, Thailand and the Philippines. The diving in this up-and-coming destination is certainly on par with these highly popular places and offers a number of different regions for diving.
To the south of Taiwan is Kenting Marine Park, a recognized and protected area well-served by dive and tour operators, and for good reason. Kenting offers absolutely magnificent diving, from well-preserved corals to an abundance of marine life in every shape, size and colour imaginable.
To the east are the islands known as Green Island and Orchid Island, currently two of the more popular locations boasting volcanic reefs teaming with pelagic life. Both Green Island and Orchid Island (Lanyu Island) are also the ideal locations to spot a number of shark species.
Off the west coast are the Penghu Islands, famous for challenging drift dives and finally, Xiao Liu Chiu is located on the south-west coast of Taiwan and is known for regular sea turtle sightings.
In recent times, Taiwan has faced the challenge of illegal fishing and although efforts are being made to rectify the impact of these illegal fishermen as well as the damage caused by over 4 million tourists every year, many fear that it may be a case of 'too little too late'. Regardless of what happens in the future, divers interested in diving in Taiwan are advised to visit sooner rather than later to get the best experience of these beautiful sites.
Although much of the diving in Taiwan is better suited for the more experienced diver due to difficult conditions and strong currents, there is still plenty to experience for beginners, especially at the shallower sites and shore dives at Kenting's Marine Park.
The Marine Park covers over 15 000 hectares of ocean with incredible diversity in corals in particular. In fact, Kenting Marine Park boasts almost 60% of the world's corals in just one location, with over 80 soft and stony coral species to look out for. Kenting is home to many steep cliffs situated right at the water's edge and provides easy access to a number of sites that are simply phenomenal, especially when you consider that Kenting is home to over 1100 species of fish.
A number of shore dives are available at Kenting, where divers are likely to see lionfish, sweetlips, angelfish and vibrant parrotfish swimming through caves and swim-throughs and other exciting underwater landscapes. The underwater life here is nothing short of spectacular, with beautiful soft and hard corals adding to the underwater paradise and the entire environment being fully supported by the rich nutrients that stem from the warm Kuroshio Current or Black Tide.
The shore dive at Kenting's main beach is particularly interesting as it is home to a resident Maori wrasse as well as large catfish and surgeonfish. Pelagics are also fairly common sightings, with reports of white, black and even umbrella swordfish sightings as well as mahi mahi, trevallies, tuna and even whales at certain times of the year.
Green Island is one of Taiwan's most famous diving locations, and it's easy to understand why locals and foreigners flock to this stunning volcanic island. An abundance of marine species can be found at the often-busy Green Island including the endangered Coconut Crab, big spotted rays, sharks and batfish too. Divers with over 100 or more logged dives can try their luck at finding the large shoals of hammerhead sharks at Shark Point, otherwise known as Gun Swei B. This great and challenging site is home to strong currents and swells, but the reward is well-worth the effort for experienced divers.
Penghu is both famous and infamous and is one of the lesser developed dive spots in Taiwan, although that does not mean that divers should give it a miss. In fact, Penghu is likely to be less crowded than more popular areas such as Green Island and Orchid Island and offers diving that is just as good. The drift diving in Penghu is fantastic, however it is well-known for having some of the strongest currents around and again is best suited for more experienced divers.
Orchid Island is a one-stop dive destination that has it all: wall dives, stunning reef dives at varying depths, a wide variety of fish species on display everywhere you look, an abundance of healthy corals and even a wreck site to explore. It is a good idea to visit the west coast of the island in winter, whilst the better diving around the north and eastern coasts of the island occurs during the summer months. Divers are regularly treated to shark, manta ray, sea turtle and dogtooth tuna sightings as well as puffer fish, lion fish and sea snakes.
Taiwan is well connected with efficient rail, ferry, road and air connections, making travelling around fairly easy, especially since most of the popular diving areas can be accessed a number of ways using various modes of reliable transport. The only major concern for travellers is some of the lesser-known but somewhat familiar-sounding airlines. It is safest to stick with well-known international airlines that you trust, or better yet, make use of a travel agent to assist you.
Once in Taiwan, it is relatively easy and cost-effective to travel around using Taiwan's reliable network of fast trains, whilst other travellers make use of the cheap, hassle-free car hire options from airports and major cities. Domestic flights are also relatively inexpensive and easy to arrange, however when making arrangements to travel to the islands it is best to use an experienced tour operator. Many experienced operators will offer package deals including domestic flights to the islands, and for many visitors this is the easiest option as arranging flights to the islands can be rather tricky without an experienced travel operator.
From Taiwan's International Airport, an hour's bus ride will get you to Songshan Airport in Taipei, which is the main entry point for divers as it is where many of the domestic flights to the islands depart from. There are four major domestic airlines departing from the airport in Taipei, and these will allow visitors to travel to areas such as Taichung, Penghu, Hualien, Kaohsiung (for Kenting), Kinmen, Tainan and Taitung (for Green Island and Orchid Island).
It is also possible to reach Kenting and its Marine Park region by rail, as there is a train station and airport in Kaohsiung. From there, take one of the many frequent busses to Hengshung.
Travelling by ferry is a good option when looking to get to Green Island, although the 1-hour ride is renowned for being choppy so visitors may want to bring seasickness tablets along just in case. The ferry departs from Taitung town on the east coast or from Fugang Harbour further north. In order to avoid the rough ferry ride, it is possible to fly from Taitung, however tickets for this 15 minute flight are usually booked well in advance.
The easiest way to reach the Penghu Islands is by air, with regular flights from Taipei and Kaohsiung. It is also possible to take a 3.5 hour ferry from Kaohsiung or a shorter 1.5 hour ferry from the port of Budai near Chiayi.
It is also possible to reach Orchid Island by a short connecting flight from Taipei, or via ferry from Fugang.
Thanks to an increase in visiting divers from abroad, dive shops are now scattered all over the more popular diving destinations and can assist divers with equipment rental, servicing, transport to and from the dive sites as well as additional dive training. In fact, Taiwan is one of the cheapest places to advance your diving qualification, and is a good test of your skills due to the strong currents and often difficult conditions.
Accommodation options are endless and suitable for a wide range of budgets. Options include dive resorts, hotels, hostels and even boutique hotels.
Although Taiwan is a small country, it sure packs a punch when you consider the weather. Its sub-tropical climate offers visitors the choice of extremely hot and humid summers or cool, often wet winters with typhoons prevalent between the end summer months and the middle of autumn. Typhoons are more likely to affect the east coast of Taiwan, however, so if choosing to visit during these months it is highly recommended to opt for dive sites on the southern or western coasts to avoid the typhoon season.
The best time to travel to Taiwan, especially if you are keen to get the best diving experience possible, is likely to be in the autumn or spring, although the autumn months tend to be more reliable than the spring months. Spring weather tends to be rather sporadic, with plenty of unpredictable rain which will undoubtedly have an effect on underwater visibility. Many people choose to visit in the height of summer, which brings with it high levels of humidity and an increase in prices, especially around the islands.
The water temperature remains fairly constant between 22-29°C, and most divers find a 3mm wetsuit to be more than enough to keep warm and comfortable whilst underwater.


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Home Newsroom News and press releases OSCE Mediterranean Points of Contact Network on border security and management complete training course on passenger data systems in Bari, Italy
OSCE Secretariat
OSCE Mediterranean Points of Contact Network on border security and management complete training course on passenger data systems in Bari, Italy
Media accreditation to OSCE events
OSCE Live
OSCE Mediterranean Points of Contact during a study visit to Guardia di Finanza's Air Unit, Bari, 11 June 2019. (Guardia di Finanza) Photo details
Members of the OSCE Mediterranean Points of Contact Network on border security and management completed an intensive and interactive four-day training course on Advance Passenger Information (API) and Passenger Name Record (PNR) systems on 14 June 2019 in Bari, Italy.
The course was organized by the Border Security and Management Unit of the OSCE Transnational Threats Department in close co-operation with Italy's Guardia di Finanza (GdF), a law enforcement agency under the authority of the country's Minister of Economy and Finance.
The training was conducted by representatives of INTERPOL, the International Organisation for Migration, the United Nations Office of Counter Terrorism, commercial service providers as well as relevant experts on passenger data from the Netherlands, the United Kingdom and the United States, together with staff from the OSCE's Border Security and Management Unit.
The participants, represented by border and customs services of Algeria, Israel, Jordan, Morocco and Tunisia, were familiarized with current trends challenges as well as the international legal framework related to foreign terrorist fighters in the Mediterranean region.
In-depth presentations focused on both the API and PNR systems and participants completed practical exercises in developing a roadmap for setting up an API system for preventing the movement of foreign terrorist fighters. They discussed the national security and data protection issues that need to be taken into account when implementing such a system.
An API system is an electronic system through which biographic data from a traveller's passport and flight details are collected by airlines and transmitted to the relevant entities such as border control agencies before departure or arrival.
The highlight of the training was a study visit to the Guardia di Finanza's air unit in Bari where participants were introduced to Italy's maritime human rights compliant border protection system. "We truly hope that this study visit gave the OSCE Mediterranean Partners for Co-operation the possibility to learn how Guardia di Finanza's agents actually ensure border security with particular reference to maritime border protection in the Mediterranean", said Lieutenant Colonel Michele Vidoni, Head of International Relations unit within the Second Department of Guardia di Finanza's Headquarters, also stressing that the GdF and the OSCE will continue and strengthen their co-operation in the framework of the Memorandum of Understanding between the organisations "with the purpose to fight against transnational crimes on the international level".
This training course was the sixth activity of a multi-year OSCE initiative aimed at strengthening co-operation mechanisms between the border security and management agencies of the OSCE participating States and the Mediterranean Partners for Co-operation. The OSCE Mediterranean Points of Contact Network on border security and management was established in 2017.
Communication and Media Relations Section
Phone: + 43 676 71 74 592
press@osce.org
Sign up to receive OSCE Press Releases and Media Advisories by email as soon as they have been published.


Consequently, we believe GPs should consider testing patients with CRS for immunodeficiency, including serum immunoglobulins, lymphocyte subset analysis and vaccine antibody responses, particularly if there are other clinical red flags such as recurrent, deep-seated, unusual or prolonged infections, or a suggestive family history. Consideration must also be given to secondary causes of immunodeficiencies (eg human immunodeficiency virus (HIV), immunosuppressive drugs) and, where possible, managed accordingly. A referral to a clinical immunologist or other specialist may be in order as appropriate.
Morcom S, Phillips N, Pastuszek A, Timperley D. Sinusitis. Aust Fam Physician 2016;45(6):374–77.
Dykewicz MS, Hamilos DL. Rhinitis and sinusitis. J Allergy Clin Immunol 2010;125(2 Suppl 2):S103–15.
Hamilos DL. Chronic rhinosinusitis patterns of illness. Clin Allergy Immunol 2007;20:1–13.
Vanlerberghe L, Joniau S, Jorissen M. The prevalence of humoral immunodeficiency in refractory rhinosinusitis: a retrospective analysis. B-ENT 2006;2(4):161–66.
Alqudah M, Graham SM, Ballas ZK. High prevalence of humoral immunodeficiency patients with refractory chronic rhinosinusitis. Am J Rhinol Allergy 2010;24(6):409–12.
Individuals with pre-existing or acquired immunodeficiencies are at risk of developing CRS. In a study by Chee et al1 of 79 patients with CRS (with or without nasal polyps) refractory to medical treatment, 30% had decreased T-cell function and 20% had an immunoglobulin deficiency. Common variable immunodeficiency was present in 10%.
A meta-analysis by Schwitzguébel et al2 revealed immunoglobulin deficiencies in 13% of patients with refractory CRS, and 23% of patients with difficult to treat CRS (persistent symptoms despite surgery and ongoing medical therapy). As such, investigation of possible immunodeficiencies should be considered if adequate medical treatment has failed, or for individuals with multiple infections (otitis media, bronchitis, pneumonia).
The benefits of surgery in individuals with a non-acquired immunodeficiency are similar to that experienced by individuals with normal immune function3 and, as such, treatment should be approached in a similar fashion.
The presence of a sinus mycetoma (fungal ball) is not suggestive of an underlying immunodeficiency, with no difference in immunoglobulin levels detected compared to CRS.4 Acquired immunodeficiencies (HIV infection, haematpoetic stem cell transplants) may predispose to acute invasive fungal rhinosinusitis, with a significant risk of mortality. Treatment in this setting should be multimodal, with surgical debridement and antifungal treatment, along with management of the underlying immunodeficiency state.
Chee L, Graham SM, Carothers DG, Ballas ZK. Immune dysfunction in refractory sinusitis in a tertiary care setting. The Laryngoscope 2001 Feb;111(2):233–35.
Schwitzguébel AJ, Jandus P, Lacroix JS, Seebach JD, Harr T. Immunoglobulin deficiency in patients with chronic rhinosinusitis: Systematic review of the literature and meta-analysis. J Allergy Clin Immunol 2015;136(6):1523–31.
Khalid AN, Mace JC, Smith TL. Outcomes of sinus surgery in ambulatory patients with immune dysfunction. Am J Rhinol Allergy 2010;24(3):230–33.


Your anger is close to the surface. You might need to deal with a different set of problems than usual, and could feel frustrated. Know that you do not have the full picture. Focus on a friendship; this person often comes through for you.
Tonight: You have a lot of ground to cover.


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Q: How to get a data from terraform I need a help in terraform.
I have created a ec2 instance using terraform ,after creating the instance I am login to the aws console and added the storage in the instance what I have created the instance.
Now I made some changes in to the terraform code, after that I ran the terraform plan it is showing, the deleting the storage.
How can I run the terraform apply without deleting the storage.

A: Its a vary bad practice to manually change resource managed by TF as it results in a drift. Please check TF docs how to deal with that:

*

*Detecting and Managing Drift with Terraform

A: As the other answer states, it is bad practice to manually change a Terraform managed resource for the reason you are experiencing.
One possibility that you could look at is bringing the storage that you added under the management of Terraform, this is done by terraform import command.
An example, assuming you have added an EBS volume, would be to declare a Terraform resource for the EBS volume and it's attachement to your instance:
resource "aws_ebs_volume" "example" {
  size = 20
  # other props to match real instance
}

resource "aws_volume_attachment" "ebs_att" {
  device_name = "/dev/sdf"
  volume_id   = aws_ebs_volume.example.id
  instance_id = aws_instance.example.id
}

and then import these using the import commands, the details required for each resource varies but can be found in the documentation for each resource.
$ terraform import aws_ebs_volume.example vol-0123456789abc
$ terraform import aws_volume_attachment.example /dev/sdf:vol-0123456789abc:i-876543321

redpajama_set_name: RedPajamaStackExchange


On macOS libaacs has to unmount the disc for exclusive access and then mount it again when done with it. Both the mounting and unmounting are non-blocking, which leads to issues especially when mounting the disc again in the end because the function returns before the disc is actually mounted and users of the library then will try to access a not yet mounted device without a way to easily figure out when it is mounted again.
Additionally it seems that the way is_mounted is set is racy, though probably not as big of a problem given how the code works.
An easy fix would be to block in iokit_mount until the device is actually mounted.
The same issue exists in the unmount function but luckily it seems it is fast enough to not cause an issue.


text:
President Trump makes unexpected appearance at United Nations climate summit
New Delhi, Delhi, India Published: Sep 23, 2019, 10:57 PM(IST)
US President Donald Trump (C) attends the UN Climate Action Summit. Photograph:( AFP )
Trump spent a few minutes in the hall of the General Assembly where he applauded Indian Prime Minister Narendra Modi's speech then left.
In a surprising turn of events, President Donald Trump made a brief unscheduled appearance on Monday at the United Nations climate summit, which he had been expected to skip entirely.
Trump, who has repeatedly expressed doubt about the overwhelming scientific consensus on manmade causes of global warming, spent a few minutes in the hall of the General Assembly where he applauded Indian Prime Minister Narendra Modi's speech, then left.
Dozens of world leaders were discussing measures to try and slow down ever-more dangerous trends in climate change. Trump, who withdrew the United States from a major international pact on reducing greenhouse gas outputs, did not speak.
Watch: Committed to fight radical Islamic terror: Donald Trump at 'Howdy, Modi' event
Earlier, opening the summit, Secretary-General Antonio Guterres said, "The climate emergency is a race we are losing, but it is a race we can win."
French President Emmanuel Macron invited his counterparts from Chile, Colombia and Bolivia to a meeting where $500 million in extra funds were pledged by the World Bank, Inter-American Development Bank, and non-profit Conservation International to protect the world's rainforests.
Fewer than half the 136 heads of government or state in New York this week to attend the UN General Assembly will be present Monday.


Alamgir mosque is famed for its exquisite architecture and enviable location. The mosque is situated on the edge of River Ganga and enjoys a picturesque scene. Said to be built by Mughal Emperor Aurangzeb who is believed to have demolished the old Kashivishwanath Temple and constructed this mosque in its place; Alamgir mosque is a fine piece of architecture. The mosque is also known as Gyanvapi mosque due to the well of the same name situated close to it. One can notice the influence of North Indian or Hindu architecture in this mosque; however most of the mosque is done in Islam architecture. Reckoned to be the oldest ghat in Varanasi, Dasaswamendha Ghat is where all the action takes place. The ghat is situated near the Vishwanath temple and is famous for its evening aarti. Often crowded with sadhus, sanayasis, beggars and devotees, this ghat is an important landmark of Varanasi city. During the evening aarti, devotees leave earthern lamps in the water to float creating a beautiful scene which is unmatched.
Among the many sightseeing places in Varanasi is the Tulsi Manas Temple, the place where Tulsidas wrote the Hindu epic Ramayana in an Awadhi dialect of the Hindi language. The temple was funded by the Birla family and constructed in white marble in 1964. Verses and scenes from Ramcharitmanas are engraved on the mandir walls. extra details at Varanasi boat ride.
Dashashwamedh Ghat This is the icon ghat of Varanasi. This is the location of Ganga Arti, the most photographed ceremony of India. It is said that Lord Brahma created this ghat by sacrificing ten horses. By sunset, pilgrims flock this place to watch the arti ritual. Some even hire a boat to watch the ritual from the water. If you want to enjoy a clear view of the ritual, dine at any of the rooftop restaurants near the ghat. The ghat thrives with touristy business. The boat owners will lurk you into taking a Ganges boat ride, while the shop keepers try to make you tour their shops. You can also find some massage practitioners propagating their business in this ghat.
The cruise Alaknanda has a full-fledged will also have (vegetarian/ vegan) galley. The cruise consists of latest navigational systems and onboard GPS, free WiFi, a skid-proof afterdeck, four bio-toilets.
Chandraprabha sanctuary is situated to the Southeast of Varanasi. Though one of India's lesser-known sanctuaries, Chandraprabha is endowed with beautiful picnic spots, dense forests, and scenic waterfalls like Rajdari and Devdari that attract a large number of tourists.


text:
Q: Is varargs array creation lazy? Consider the following code:
void someMethod(Object... varargParam){
    if(someCondition){
        doSomethingWith(varargParam);
    }
}

If someCondition resolves to false, varargParam is unused. Will the Object array still be created or is this creation postphoned to the first use?

A: varargParam is created when the call happens, also if not used in the method itself.
From JLS 15.14.2:

If the method being invoked is a variable arity method m, it necessarily has n > 0 formal parameters. The final formal parameter of m necessarily has type T[] for some T, and m is necessarily being invoked with k ≥ 0 actual argument expressions.


In order to maintain the business in the perfect position at the marketing place, you can consider the major factors involved in the business. The people opt for starting the different kinds of the business. Today, the people are willing to start the cloth business to get the maximum profit. The retailers get the best techniques for the sales increase purpose. The retailers must keep up the excellent and knowledge to make the money in a different way. The Kidswear manufacturers Suncity make the best kids cloth that comes up with the fine design and color. You can learn something and do the things in your business.
The retailers see the requirements of the industry. You can get the competitive edge for the business promotion. In order to any type of the business, you must have the excellent knowledge in the business and drive the maximum sales. You can know what type of things that the industry need. You can learn the industry process and access the tips to attract the customer. Some business fails due to reasons like lack of knowledge in the industry, bad marketing strategy, improper capitalization and others. These are the major factors that affect the sales. You can get rid of all the things and implement the new strategy to increase the sales.
The business cannot run without the cash flow. The cash flow is the mandatory factor to achieve the success. You can manage the business financially healthy with this vital component. You can reach the customer worldwide and increase the cash flow. You can offer the different option to get the best clothes in your store. You can never worry about to run the business out of the money.
The brand image plays a vital role in the business. It is the great concern of function the business with the right marketing efforts and material. In this way, the customers believe the product to buy it very quickly. The customers access your store in the form of website appearance, store location, discount, deals, products, customer services and so on. The retailers follow the important terms that relevant to the business. You can meet the customers demand with the great supply of the materials.
Now, the majority of the retailers provide the great offers to the customer. You can keep an eye on the famous sales promotion. You can provide buy one and get one offer, buy one and half price offer, discount, and deals. The retailers give this kind of offer during the weekend and special occasion time.
The retailers provide the stunning offers and deals to the customer for every purchase. It is the great option for the customer to buy more products at the same time. The retailers understand the practical approach depending on the function of supply. You can offer the best price products to catch the audience attention in your business.


text:
Series Preview: Astros at Angels
By John Burgess
HOUSTON ASTROS (3-6) VS LOS ANGELES ANGELS OF ANAHEIM (2-7)
ANGEL STADIUM OF ANAHEIM IN ANAHEIM, CALIFORNIA
The Houston Astros picked up their season's first series win Wednesday night with an 8-3 victory over the Seattle Mariners. The Astros now head into Anaheim, California for the first time since 2007. Houston has gotten a heavy dose of the AL West to start the season, and will conclude their divisional introductions with the Los Angeles Angels of Anaheim. The Astros' offense exploded for 24 runs on 37 hits, including eight home runs, in the final two games of the Mariners' series. They will hope to continue their offensive prowess versus an Angels staff that is missing it's ace, Jered Weaver.
Game 1: April 12th at 9:05 CT
Bud Norris (1-1, 3.18 ERA) vs Tommy Hanson (1-0, 4.50 ERA)
Troy Taormina-USA TODAY Sports
Tommy Hanson made his first start as an Angel on April 6th in Arlington against the Rangers and had a strong outing. He surrendered three runs over six innings pitched, and gave up two home runs, which isn't too alarming in that stadium. Brett Wallace is the only Astros' hitter with more than five career at-bats against Hanson, and he is 0-9 with five strikeouts. Game one is probably not a good time to ease the slumping Wallace back into the starting lineup.
Bud Norris will be making his third start of the young season, and his first road start. Norris started the season strong against the Rangers on Opening Day, and nearly replicated those numbers against the Athletics on April 6th. Last season Norris' road numbers were horrible (3-12, 6.94) so he will look to turn that around. The only Angels hitter with a considerable amount of at-bats versus Norris is Albert Pujols, who has put up just a .192 average in 26 career ABs. Norris has yet to go longer than 5.2 innings this season, and with the Astros' weak bullpen that is something that needs to change immediately.
Lucas Harrell (0-2, 7.84 ERA) vs Garrett Richards (0-0, 2.08 ERA)
Garrett Richards is making his first start of the season, taking the place of the injured Jered Weaver. He has made four appearances this year out of the bullpen. Richards has made just twelve starts for the Angels since 2011 with very limited success. He has a mid-90s fastball, a cutter, a slider that he uses frequently, and a change up. Because Richards has been working out of the pen, he shouldn't be expected to go much more than four innings before the bullpen takes over.
Lucas Harrell had some serious control problems in his last start against Oakland. He lasted just 4.1 innings, allowing eight runs on seven hits while walking five. He consistently fell behind in the count and then had to give in to the hitter, and it ended up costing him as he gave up three long balls. Walks + home runs = disaster. Harrell has little to no experience against any of the hitters in the Angels lineup, but if he can keep the ball down and limit the number of walks he should be fine.
Game 3: Sunday, April 14th at 2:35 CT
Philip Humber (0-2, 3.09 ERA) vs C.J. Wilson (0-0, 5.25 ERA)
Gary A. Vasquez-USA TODAY Sports
C.J. Wilson made his last start against Oakland on Tuesday and had a rough start to the game before settling down. He went six innings, allowing six hits and four runs while striking out seven. Wilson retired 15 of the last 17 hitters he faced. Wilson relies heavily on his fastball-curveball combination, and over his career he's gotten just over 50% of the hitters he's faced to put the ball on the ground. C.J. Wilson is the lone lefty starter that the Astros will face in this series.
Philip Humber has had back-to-back strong starts, yet he's been stuck with two losses. In his last start versus the Mariners, he allowed three earned over six innings while striking out two. Humber's average fastball velocity is down from last season by over 1 mph in his first two starts. He has also thrown nearly 19% more breaking pitches this season than he has averaged over his career. I would like to see Humber, as well as the rest of the Astros' starters, be more efficient so that they can limit the bullpen's usage.
Worth Noting:
The Astros' lineup scored just 17 runs through the first seven games of the season before exploding for 24 runs in their last two games. That has raised Houston's Pythagorean Expectancy from a winning percentage of .160 to a .393, which would allow them to avoid three straight 100 loss seasons.
Albert Pujols has played in 177 career games against the Astros and has a .311 average with 42 home runs and 112 RBI during that time.
The Angels have the highest payroll in the American League West at $137,271,250. That means there is a $111,165,650 difference between the Astros and Angels payrolls.


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