ConQUR: Mitigating Delusional Bias in Deep Q-Learning
Delusional bias is a fundamental source of error in approximate Q-learning. To date, the only techniques that explicitly address delusion require comprehensive search using tabular value estimates. In this paper, we develop efficient methods to mitigate delusional bias by training Q-approximators with labels that are *consistent* with the underlying greedy policy class. We introduce a simple penalization scheme that encourages Q-labels used across training batches to remain (jointly) consistent with the expressible policy class. We also propose a search framework that allows multiple Q-approximators to be generated and tracked, thus mitigating the effect of premature (implicit) policy commitments. Experimental results demonstrate that these methods can improve the performance of Q-learning in a variety of Atari games, sometimes dramatically.
This paper focuses on addressing the delusional bias problem in deep Q-learning, and propose a general framework (ConQUR) for integrating policy-consistent backups with regression-based function approximation for Q-learning and for managing the search through the space of possible regressors. Specifically, it proposes a soft consistency penalty to alleviate the delusional bias problem while avoiding the expensive exact consistency testing. This penalty encourages the parameters of the model to satisfy the consistency condition when solving the MSBE problem. Pros: The soft penalization itself is already shown to be effective in further improving any regression-based Q-learning algorithms (e.g., DQN and DDQN). When combining the soft consistency penalty and the search procedure, it is shown to significantly outperform the DQN and DDQN baselines, which is impressive. This work presents novel idea and solid supporting experiments. Cons: The major experimental results of the paper are in Table 4 of Appendix D. This is not a good effort to save space by moving the most important results into appendix. At least a selected set of results should be presented in the main paper rather than in appendices. One alternative approach is the authors plot a bar figure to demonstrate the performance of different algorithms on different Atari games. Other comments: • Fig. 2 two is a bit hard to read due to too many curves. • Standard baseline results other than DQN and DDQN should also be listed, in order to demonstrate that solving delusional bias could make Q-learning more competitive than alternatives (e.g., A3C, PPO, TRPO).
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
ConQUR: Mitigating Delusional Bias in Deep Q-Learning
Delusional bias is a fundamental source of error in approximate Q-learning. To date, the only techniques that explicitly address delusion require comprehensive search using tabular value estimates. In this paper, we develop efficient methods to mitigate delusional bias by training Q-approximators with labels that are *consistent* with the underlying greedy policy class. We introduce a simple penalization scheme that encourages Q-labels used across training batches to remain (jointly) consistent with the expressible policy class. We also propose a search framework that allows multiple Q-approximators to be generated and tracked, thus mitigating the effect of premature (implicit) policy commitments. Experimental results demonstrate that these methods can improve the performance of Q-learning in a variety of Atari games, sometimes dramatically.
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 paper focuses on addressing the delusional bias problem in deep Q-learning, and propose a general framework (ConQUR) for integrating policy-consistent backups with regression-based function approximation for Q-learning and for managing the search through the space of possible regressors. Specifically, it proposes a soft consistency penalty to alleviate the delusional bias problem while avoiding the expensive exact consistency testing. This penalty encourages the parameters of the model to satisfy the consistency condition when solving the MSBE problem. Pros: The soft penalization itself is already shown to be effective in further improving any regression-based Q-learning algorithms (e.g., DQN and DDQN). When combining the soft consistency penalty and the search procedure, it is shown to significantly outperform the DQN and DDQN baselines, which is impressive. This work presents novel idea and solid supporting experiments. Cons: The major experimental results of the paper are in Table 4 of Appendix D. This is not a good effort to save space by moving the most important results into appendix. At least a selected set of results should be presented in the main paper rather than in appendices. One alternative approach is the authors plot a bar figure to demonstrate the performance of different algorithms on different Atari games. Other comments: • Fig. 2 two is a bit hard to read due to too many curves. • Standard baseline results other than DQN and DDQN should also be listed, in order to demonstrate that solving delusional bias could make Q-learning more competitive than alternatives (e.g., A3C, PPO, TRPO).
assistant
This paper focuses on addressing the delusional bias problem in deep Q-learning, and propose a general framework (ConQUR) for integrating policy-consistent backups with regression-based function approximation for Q-learning and for managing the search through the space of possible regressors. Specifically, it proposes a soft consistency penalty to alleviate the delusional bias problem while avoiding the expensive exact consistency testing. This penalty encourages the parameters of the model to satisfy the consistency condition when solving the MSBE problem. Pros: The soft penalization itself is already shown to be effective in further improving any regression-based Q-learning algorithms (e.g., DQN and DDQN). When combining the soft consistency penalty and the search procedure, it is shown to significantly outperform the DQN and DDQN baselines, which is impressive. This work presents novel idea and solid supporting experiments. Cons: The major experimental results of the paper are in Table 4 of Appendix D. This is not a good effort to save space by moving the most important results into appendix. At least a selected set of results should be presented in the main paper rather than in appendices. One alternative approach is the authors plot a bar figure to demonstrate the performance of different algorithms on different Atari games. Other comments: • Fig. 2 two is a bit hard to read due to too many curves. • Standard baseline results other than DQN and DDQN should also be listed, in order to demonstrate that solving delusional bias could make Q-learning more competitive than alternatives (e.g., A3C, PPO, TRPO).