Bayesian Residual Policy Optimization: Scalable Bayesian Reinforcement Learning with Clairvoyant Experts
Informed and robust decision making in the face of uncertainty is critical for robots that perform physical tasks alongside people. We formulate this as a Bayesian Reinforcement Learning problem over latent Markov Decision Processes (MDPs). While Bayes-optimality is theoretically the gold standard, existing algorithms do not scale well to continuous state and action spaces. We propose a scalable solution that builds on the following insight: in the absence of uncertainty, each latent MDP is easier to solve. We split the challenge into two simpler components. First, we obtain an ensemble of clairvoyant experts and fuse their advice to compute a baseline policy. Second, we train a Bayesian residual policy to improve upon the ensemble*s recommendation and learn to reduce uncertainty. Our algorithm, Bayesian Residual Policy Optimization (BRPO), imports the scalability of policy gradient methods as well as the initialization from prior models. BRPO significantly improves the ensemble of experts and drastically outperforms existing adaptive RL methods.
In this paper, the authors motivate and propose a learning algorithm, called Bayesian Residual Policy Optimization (BRPO), for Bayesian reinforcement learning problems. Experiment results are demonstrated in Section 5. The paper is well written in general, and the proposed algorithm is also interesting. However, I think the paper suffers from the following limitations: 1) This paper does not have any theoretical analysis or justification. It would be much better if the authors can rigorously prove the advantages of BRPO under some simplifying assumptions. 2) It would be better if the authors can provide more experiment results, like experiment results in more games.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Bayesian Residual Policy Optimization: Scalable Bayesian Reinforcement Learning with Clairvoyant Experts
Informed and robust decision making in the face of uncertainty is critical for robots that perform physical tasks alongside people. We formulate this as a Bayesian Reinforcement Learning problem over latent Markov Decision Processes (MDPs). While Bayes-optimality is theoretically the gold standard, existing algorithms do not scale well to continuous state and action spaces. We propose a scalable solution that builds on the following insight: in the absence of uncertainty, each latent MDP is easier to solve. We split the challenge into two simpler components. First, we obtain an ensemble of clairvoyant experts and fuse their advice to compute a baseline policy. Second, we train a Bayesian residual policy to improve upon the ensemble*s recommendation and learn to reduce uncertainty. Our algorithm, Bayesian Residual Policy Optimization (BRPO), imports the scalability of policy gradient methods as well as the initialization from prior models. BRPO significantly improves the ensemble of experts and drastically outperforms existing adaptive RL methods.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



In this paper, the authors motivate and propose a learning algorithm, called Bayesian Residual Policy Optimization (BRPO), for Bayesian reinforcement learning problems. Experiment results are demonstrated in Section 5. The paper is well written in general, and the proposed algorithm is also interesting. However, I think the paper suffers from the following limitations: 1) This paper does not have any theoretical analysis or justification. It would be much better if the authors can rigorously prove the advantages of BRPO under some simplifying assumptions. 2) It would be better if the authors can provide more experiment results, like experiment results in more games.
assistant
In this paper, the authors motivate and propose a learning algorithm, called Bayesian Residual Policy Optimization (BRPO), for Bayesian reinforcement learning problems. Experiment results are demonstrated in Section 5. The paper is well written in general, and the proposed algorithm is also interesting. However, I think the paper suffers from the following limitations: 1) This paper does not have any theoretical analysis or justification. It would be much better if the authors can rigorously prove the advantages of BRPO under some simplifying assumptions. 2) It would be better if the authors can provide more experiment results, like experiment results in more games.