Combining policy gradient and Q-learning
Policy gradient is an efficient technique for improving a policy in a reinforcement learning setting. However, vanilla online variants are on-policy only and not able to take advantage of off-policy data. In this paper we describe a new technique that combines policy gradient with off-policy Q-learning, drawing experience from a replay buffer. This is motivated by making a connection between the fixed points of the regularized policy gradient algorithm and the Q-values. This connection allows us to estimate the Q-values from the action preferences of the policy, to which we apply Q-learning updates. We refer to the new technique as ‘PGQL’, for policy gradient and Q-learning. We also establish an equivalency between action-value fitting techniques and actor-critic algorithms, showing that regularized policy gradient techniques can be interpreted as advantage function learning algorithms. We conclude with some numerical examples that demonstrate improved data efficiency and stability of PGQL. In particular, we tested PGQL on the full suite of Atari games and achieved performance exceeding that of both asynchronous advantage actor-critic (A3C) and Q-learning.
This is a very nicely written paper which unifies some value-based and policy-based (regularized policy gradient) methods, by pointing out connections between the value function and policy which have not been established before. The theoretical results are new and insightful, and will likely be useful in the RL field much beyond the specific algorithm being proposed in the paper. This being said, the paper does exploit the theory to produce a unified version of Q-learning and policy gradient, which proves to work on par or better than the state-of-art algorithms on the Atari suite. The empirical section is very well explained in terms of what optimization were done. One minor comment I had was related to the stationary distribution used for a policy - there are subtleties here between using a discounted vs non-discounted distribution which are not crucial in the tabular case, but will need to be addressed in the long run in the function approximation case. This being said, there is no major problem for the current version of the paper. Overall, the paper is definitely worthy of acceptance, and will likely influence a broad swath of RL, as it opens the door to further theoretical results as well as algorithm development.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Combining policy gradient and Q-learning
Policy gradient is an efficient technique for improving a policy in a reinforcement learning setting. However, vanilla online variants are on-policy only and not able to take advantage of off-policy data. In this paper we describe a new technique that combines policy gradient with off-policy Q-learning, drawing experience from a replay buffer. This is motivated by making a connection between the fixed points of the regularized policy gradient algorithm and the Q-values. This connection allows us to estimate the Q-values from the action preferences of the policy, to which we apply Q-learning updates. We refer to the new technique as ‘PGQL’, for policy gradient and Q-learning. We also establish an equivalency between action-value fitting techniques and actor-critic algorithms, showing that regularized policy gradient techniques can be interpreted as advantage function learning algorithms. We conclude with some numerical examples that demonstrate improved data efficiency and stability of PGQL. In particular, we tested PGQL on the full suite of Atari games and achieved performance exceeding that of both asynchronous advantage actor-critic (A3C) and Q-learning.
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>



This is a very nicely written paper which unifies some value-based and policy-based (regularized policy gradient) methods, by pointing out connections between the value function and policy which have not been established before. The theoretical results are new and insightful, and will likely be useful in the RL field much beyond the specific algorithm being proposed in the paper. This being said, the paper does exploit the theory to produce a unified version of Q-learning and policy gradient, which proves to work on par or better than the state-of-art algorithms on the Atari suite. The empirical section is very well explained in terms of what optimization were done. One minor comment I had was related to the stationary distribution used for a policy - there are subtleties here between using a discounted vs non-discounted distribution which are not crucial in the tabular case, but will need to be addressed in the long run in the function approximation case. This being said, there is no major problem for the current version of the paper. Overall, the paper is definitely worthy of acceptance, and will likely influence a broad swath of RL, as it opens the door to further theoretical results as well as algorithm development.
assistant
This is a very nicely written paper which unifies some value-based and policy-based (regularized policy gradient) methods, by pointing out connections between the value function and policy which have not been established before. The theoretical results are new and insightful, and will likely be useful in the RL field much beyond the specific algorithm being proposed in the paper. This being said, the paper does exploit the theory to produce a unified version of Q-learning and policy gradient, which proves to work on par or better than the state-of-art algorithms on the Atari suite. The empirical section is very well explained in terms of what optimization were done. One minor comment I had was related to the stationary distribution used for a policy - there are subtleties here between using a discounted vs non-discounted distribution which are not crucial in the tabular case, but will need to be addressed in the long run in the function approximation case. This being said, there is no major problem for the current version of the paper. Overall, the paper is definitely worthy of acceptance, and will likely influence a broad swath of RL, as it opens the door to further theoretical results as well as algorithm development.