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:

Trading off exploration and exploitation in an unknown environment is key to maximising expected return during learning.
A Bayes-optimal policy, which does so optimally, conditions its actions not only on the environment state but on the agent's uncertainty about the environment.
Computing a Bayes-optimal policy is however intractable for all but the smallest tasks.
In this paper, we introduce variational Bayes-Adaptive Deep RL (variBAD), a way to meta-learn to perform approximate inference in an unknown environment, and incorporate task uncertainty directly during action selection.
In a grid-world domain, we illustrate how variBAD performs structured online exploration as a function of task uncertainty.
We also evaluate variBAD on MuJoCo domains widely used in meta-RL and show that it achieves higher return during training than existing methods.
Reinforcement learning (RL) is typically concerned with finding an optimal policy that maximises expected return for a given Markov decision process (MDP) with an unknown reward and transition function.
If these were known, the optimal policy could in theory be computed without environment interactions.
By contrast, learning in an unknown environment usually requires trading off exploration (learning about the environment) and exploitation (taking promising actions).
Balancing this trade-off is key to maximising expected return during learning, which is desirable in many settings, particularly in high-stakes real-world applications like healthcare and education (Yauney & Shah, 2018; Liu et al., 2014) .
A Bayes-optimal policy, which does this trade-off optimally, conditions actions not only on the environment state but on the agent's own uncertainty about the current MDP.
In principle, a Bayes-optimal policy can be computed using the framework of Bayes-adaptive Markov decision processes (BAMDPs) (Martin, 1967; Duff & Barto, 2002) , in which the agent maintains a belief distribution over possible environments.
Augmenting the state space of the underlying MDP with this belief yields a BAMDP, a special case of a belief MDP (Kaelbling et al., 1998) .
A Bayes-optimal agent maximises expected return in the BAMDP by systematically seeking out the data needed to quickly reduce uncertainty, but only insofar as doing so helps maximise expected return.
Its performance is bounded from above by the optimal policy for the given MDP, which does not need to take exploratory actions but requires prior knowledge about the MDP to compute.
Unfortunately, planning in a BAMDP, i.e., computing a Bayes-optimal policy that conditions on the augmented state, is intractable for all but the smallest tasks.
A common shortcut is to rely instead on posterior sampling (Thompson, 1933; Strens, 2000; Osband et al., 2013) .
Here, the agent periodically samples a single hypothesis MDP (e.g., at the beginning of an episode) from its posterior, and the policy that is optimal for the sampled MDP is followed until the next sample is drawn.
Planning is far more tractable since it is done on a regular MDP, not a BAMDP.
However, posterior sampling's exploration can be highly inefficient and far from Bayes-optimal.
Consider the example of a gridworld in Figure 1 , where the agent must navigate to an unknown goal located in the grey area (1a).
To maintain a posterior, the agent can uniformly assign non-zero probability to cells where the goal could be, and zero to all other cells.
A Bayes-optimal strategy strategically searches the set of goal positions that the posterior considers possible, until the goal is found (1b).
Posterior sampling by contrast samples a possible goal position, takes the shortest route there, and then resamples a different goal position from the updated posterior (1c).
Doing so is much less efficient since the agent's uncertainty is not reduced optimally (e.g., states are revisited).
Average return over all possible environments, over six episodes with 15 steps each (after which the agent is reset to the starting position).
The performance of any exploration strategy is bounded above by the optimal behaviour (of a policy with access to the true goal position).
The Bayes-optimal agent matches this behaviour from the third episode, whereas posterior sampling needs six rollouts.
VariBAD closely approximates Bayes-optimal behaviour in this environment.
As this example illustrates, Bayes-optimal policies can explore much more efficiently than posterior sampling.
Hence, a key challenge is to find ways to learn approximately Bayes-optimal policies while retaining the tractability of posterior sampling.
In addition, many complex tasks pose another key challenge: the inference involved in maintaining a posterior belief, needed even for posterior sampling, may itself be intractable.
In this paper, we combine ideas from Bayesian reinforcement learning, approximate variational inference, and meta-learning to tackle these challenges, and equip an agent with the ability to strategically explore unseen (but related) environments for a given distribution, in order to maximise its expected return.
More specifically, we propose variational Bayes-Adaptive Deep RL (variBAD), a way to meta-learn to perform approximate inference on a new task 1 , and incorporate task uncertainty directly during action selection.
We represent a single MDP using a learned, low-dimensional stochastic latent variable m.
Given a set of tasks sampled from a distribution, we jointly meta-train: (1) a variational auto-encoder that can infer the posterior distribution over m in a new task while interacting with the environment, and (2) a policy that conditions on this posterior distribution over the MDP embeddings, and thus learns how to trade off exploration and exploitation when selecting actions under task uncertainty.
Figure 1e shows the performance of our method versus the hard-coded optimal (i.e., given privileged goal information), Bayes-optimal, and posterior sampling exploration strategies.
VariBAD's performance closely matches that of Bayes-optimal action selection, matching optimal performance from the third rollout.
Previous approaches to BAMDPs are only tractable in environments with small action and state spaces.
VariBAD offers a tractable and dramatically more flexible approach for learning Bayesadaptive policies tailored to the task distribution seen during training, with the only assumption that such a task distribution is available for meta-training.
We evaluate our approach on the gridworld shown above and on MuJoCo domains that are widely used in meta-RL, and show that variBAD exhibits superior exploratory behaviour at test time compared to existing meta-learning methods, achieving higher returns during learning.
As such, variBAD opens a path to tractable approximate Bayes-optimal exploration for deep reinforcement learning.
We presented variBAD, a novel deep RL method to approximate Bayes-optimal behaviour, which uses meta-learning to utilise knowledge obtained in related tasks and perform approximate inference in unknown environments.
In a didactic gridworld environment, our agent closely matches Bayesoptimal behaviour, and in more challenging MuJoCo tasks, variBAD outperforms existing methods in terms of achieved reward during a single episode.
In summary, we believe variBAD opens a path to tractable approximate Bayes-optimal exploration for deep reinforcement learning.
There are several interesting directions of future work based on variBAD.
For example, we currently do not use the decoder at test time.
One could instead use the decoder for model-predictive planning, or to get a sense for how wrong the predictions are (which might indicate we are out of distribution, and further training is necessary).
Another exciting direction for future research is considering settings where the training and test distribution of environments are not the same.
Generalising to out-of-distribution tasks poses additional challenges and in particular for variBAD two problems are likely to arise: the inference procedure will be wrong (the prior and/or posterior update) and the policy will not be able to interpret a changed posterior.
In this case, further training of both the encoder/decoder might be necessary, together with updates to the policy and/or explicit planning.
VariBAD opens a path to tractable approximate Bayes-optimal exploration for deep RL using ideas from meta-learning, Bayesian RL, and approximate variational inference.