Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Conventional deep reinforcement learning typically determines an appropriate primitive action at each timestep, which requires enormous amount of time and effort for learning an effective policy, especially in large and complex environments.
To deal with the issue fundamentally, we incorporate macro actions, defined as sequences of primitive actions, into the primitive action space to form an augmented action space.
The problem lies in how to find an appropriate macro action to augment the primitive action space.  
The agent using a proper augmented action space is able to jump to a farther state and thus speed up the exploration process as well as facilitate the learning procedure.
In previous researches, macro actions are developed by mining the most frequently used action sequences or repeating previous actions.
However, the most frequently used action sequences are extracted from a past policy, which may only reinforce the original behavior of that policy.
On the other hand, repeating actions may limit the diversity of behaviors of the agent.
Instead, we propose to construct macro actions by a genetic algorithm, which eliminates the dependency of the macro action derivation procedure from the past policies of the agent.  
Our approach appends a macro action to the primitive action space once at a time and evaluates whether the augmented action space leads to promising performance or not.   
We perform extensive experiments and show that the constructed macro actions are able to speed up the learning process for a variety of deep reinforcement learning methods.
Our experimental results also demonstrate that the macro actions suggested by our approach are transferable among deep reinforcement learning methods and similar environments.
We further provide a comprehensive set of ablation analysis to validate our methodology.
Conventional deep reinforcement learning (DRL) has been shown to demonstrate superhuman performance on a variety of environments and tasks (Mnih et al., 2013 (Mnih et al., , 2015 Salimans et al., 2017; Moriarty et al., 1999; .
However, in conventional methods, agents are restricted to make decisions at each timestep, which differs much from the temporally-extended framework of decision-making in human beings.
As a consequence, traditional methods (Mnih et al., 2013 Houthooft et al., 2016) require enormous amounts of sampling data in environments where goals are hard to reach or rewards are sparse.
In complex environments where goals can only be achieved by executing a long sequence of primitive actions, it is difficult to perform exploration efficiently.
As most real-world environments are large, complex, and usually offer sparse rewards, finding an optimal policy is still hard and challenging.
It becomes crucial to explore new mechanisms to deal with these environments more efficiently and effectively.
Researchers in the past few years have attempted various techniques to expand the realm of DRL to temporally-extended frameworks (Sutton et al., 1999; Vezhnevets et al., 2016; Kulkarni et al., 2016; Bacon et al., 2017; Frans et al., 2017; Daniel et al., 2016; Florensa et al., 2017; Machado et al., 2017) .
In such frameworks, a high-level controller interacts with the environment by selecting temporal-extended policies usually named as "options".
Once an option is selected, it interacts with the environment for a certain timesteps and perform primitive actions until a termination condition for that option is met.
However, developing effective options either requires a significant amount of domain knowledge (Girgin et al., 2010) , or often restricted to low-dimensional and/or relatively simple environments only (Bacon et al., 2017; Heess et al., 2016; Kulkarni et al., 2016) .
Instead of developing options, another branch of research directions focus on constructing macro actions (Fikes and Nilsson, 1971; Siklossy and Dowson, 1977; Minton, 1985; Pickett and Barto, 2002; Botea et al., 2005; Newton et al., 2005 Newton et al., , 2007 .
A macro action (or simply "a macro") is an open-loop (DiStefano III et al., 1967 ) policy composed of a finite sequence of primitive actions.
Once a macro is chosen, the actions will be taken by the agent without any further decision making process.
Some researches in DRL attempt to construct macros from the experience of an agent (Durugkar et al., 2016; Randlov, 1999; Yoshikawa and Kurihara, 2006; Onda and Ozawa, 2009; Garcia et al., 2019) .
A key benefit of these approaches is the ease to construct a desired macro without supervision (Durugkar et al., 2016) .
However, these approaches may lead to biased macros.
For example, the most frequently used sequence of actions may not correspond to a macro that can lead the agent to outperform its past policies.
Furthermore, as agents generally perform exploration extensively in the early stages of training, the inconsistency in the early experience may perturb the construction of macros.
A few researchers proposed to employ a reduced form of macro called action repeat Sharma et al., 2017) .
In this formulation, primitive actions are repeated several times in a macro before the agent makes another decision.
However, this formulation may limit the diversity of macros.
By relaxing the agent to perform macros consisting of diversified actions, the agent is granted more chances to achieve higher performance.
In addition, there are a handful of researches that requires human supervision to derive macros for improving training efficiency.
The authors in McGovern et al. (1997) show that handcrafted macros can speed up training in certain tasks but hinder performance in others.
The authors in Heecheol et al. (2019) generate macros from expert demonstrations via a variational auto-encoder.
However, the process of obtaining such demonstrations is expensive.
It would thus be favorable if there exists a method to find a macro without human intervention.
Nevertheless, little attention has been paid to the construction of such macros.
Our goal is to develop a methodology for constructing a macro action from possible candidates.
As possible macros are allowed to have different lengths and arbitrary compositions of primitive actions, such diversified macro actions essentially form an enormous space.
We define this space as the macro action space (or simply "macro space").
Repeated action sequences are simply a small subset of the macro space.
For a specific task in an environment, we hypothesize that there are good macros and bad macros in the macro space.
Different macro actions have different performance impacts to an agent.
Good macro actions enable the agent to jump over multiple states and reach a target state quicker and easier.
On the other hand, bad macro actions may lead the agent to undesirable states.
We argue that whether a macro is good or bad can only be determined by direct evaluation.
In this study, we propose an evaluation method to test whether a macro is satisfactory for an agent to perform a specific task in an environment.
Our method first relaxes the conventional action space (Sutton and Barto, 2018 ) with a macro to form an augmented action space.
We then equip the agent with the augmented action space, and utilize the performance results as the basis for our evaluation.
In order to find a good macro in the vast macro space, a systematic method is critically important and necessary.
The method entails two prerequisites: a macro construction mechanism and a macro evaluation method.
Although the second one is addressed above, there is still a lack of an appropriate approach to construct macros.
To satisfy the above requirement, we embrace an genetic algorithm (or simply "GA") for macro construction.
GA offers two promising properties.
First, it eliminates the dependency of the macro action derivation procedure from the past policies of an agent and/or human supervision.
Second, it produces diversified macros by mutation.
In order to combine GA with our evaluation method, our approach comprises of three phases: (1) macro construction by GA; (2) action space augmentation; and (3) evaluation of the augmented action space.
Our augmented action space contains not only the original action space defined by DRL, but also the macro(s) constructed by GA.
To validate the proposed approach, we perform our experiments on Atari 2600 (Brockman et al., 2016) and ViZDoom (Kempka et al., 2016) , and compare them to two representative DRL baseline methods.
We demonstrate that our proposed method is complementary to existing DRL methods, and perform favorably against the baselines.
Moreover, we show that the choice of the macro have a crucial impact on the performance of an agent.
Furthermore, our results reveal the existence of transferability of a few macros over similar environments or DRL methods.
We additionally provide a comprehensive set of ablation analysis to justify various aspects of our approach.
The contributions of this paper are summarized as follows:
• We define the proposed approach as a framework.
• We provide a definition of macro action space.
• We introduce an augmentation method for action spaces.
• We propose an evaluation method to determine whether a macro is good or not.
• We establish a macro action construction method using GA for DRL.
• We investigate and reveal the transferability of macro actions.
The rest of this paper is organized as follows.
Section 2 explains our framework.
Section 3 describes our implementation details.
Section 4 presents our results.
Section 5 concludes.
We have formally presented a methodology to construct macro actions that may potentially improve both the performance and learning efficiency of the existing DRL methods.
The methodology falls within the scope of a broader framework that permits other possible combinations of the DRL method, the action space augmentation method, the evaluation method, as well as the macro action construction method.
We formulated the proposed methodology as a set of algorithms, and used them as the basis for investigating the interesting properties of macro actions.
Our results revealed that the macro actions constructed by our methodology are complementary to two representative DRL methods, and may demonstrate transferability among different DRL methods and similar environments.
We additionally compared our methodology against three other macro construction methods to justify our design decisions.
Our work paves a way for future research on macros and their applications.
We propose to construct macro actions by a genetic algorithm, which eliminates the dependency of the macro action derivation procedure from the past policies of the agent.