Maxmin Q-learning: Controlling the Estimation Bias of Q-learning
Q-learning suffers from overestimation bias, because it approximates the maximum action value using the maximum estimated action value. Algorithms have been proposed to reduce overestimation bias, but we lack an understanding of how bias interacts with performance, and the extent to which existing algorithms mitigate bias. In this paper, we 1) highlight that the effect of overestimation bias on learning efficiency is environment-dependent; 2) propose a generalization of Q-learning, called emph{Maxmin Q-learning}, which provides a parameter to flexibly control bias; 3) show theoretically that there exists a parameter choice for Maxmin Q-learning that leads to unbiased estimation with a lower approximation variance than Q-learning; and 4) prove the convergence of our algorithm in the tabular case, as well as convergence of several previous Q-learning variants, using a novel Generalized Q-learning framework. We empirically verify that our algorithm better controls estimation bias in toy environments, and that it achieves superior performance on several benchmark problems.
This paper proposes a novel variant of Q-learning, called Maxmin Q-learning, to address the issue of overestimation bias Q-learning suffers from (variance of the reward of best action leading to overestimated reward). The idea is to keep a number of estimators each estimated using a different sub-sample, and taking the minimum of the (maximum) reward value of each. The paper gives theoretical analyses, in terms of the reduction in the overestimation bias, as well as the convergence of a class of generalized Q-learning methods including Maxmin Q-learning. The experiment section presents a thorough evaluation of the proposed method, including how the obtained rewards vary as a function of the variance of the reward function and as a function of learning steps, as compared to a number of existing methods such as the Double Q-learning method and its variants. The experimental results are quite convincing, and the theoretical analyses seem solid. Overall this is a well balanced paper which proposes a reasonable new idea, simple but effective, backed by sound theoretical analysis and well executed experimental evaluation.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Maxmin Q-learning: Controlling the Estimation Bias of Q-learning
Q-learning suffers from overestimation bias, because it approximates the maximum action value using the maximum estimated action value. Algorithms have been proposed to reduce overestimation bias, but we lack an understanding of how bias interacts with performance, and the extent to which existing algorithms mitigate bias. In this paper, we 1) highlight that the effect of overestimation bias on learning efficiency is environment-dependent; 2) propose a generalization of Q-learning, called emph{Maxmin Q-learning}, which provides a parameter to flexibly control bias; 3) show theoretically that there exists a parameter choice for Maxmin Q-learning that leads to unbiased estimation with a lower approximation variance than Q-learning; and 4) prove the convergence of our algorithm in the tabular case, as well as convergence of several previous Q-learning variants, using a novel Generalized Q-learning framework. We empirically verify that our algorithm better controls estimation bias in toy environments, and that it achieves superior performance on several benchmark problems.
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 proposes a novel variant of Q-learning, called Maxmin Q-learning, to address the issue of overestimation bias Q-learning suffers from (variance of the reward of best action leading to overestimated reward). The idea is to keep a number of estimators each estimated using a different sub-sample, and taking the minimum of the (maximum) reward value of each. The paper gives theoretical analyses, in terms of the reduction in the overestimation bias, as well as the convergence of a class of generalized Q-learning methods including Maxmin Q-learning. The experiment section presents a thorough evaluation of the proposed method, including how the obtained rewards vary as a function of the variance of the reward function and as a function of learning steps, as compared to a number of existing methods such as the Double Q-learning method and its variants. The experimental results are quite convincing, and the theoretical analyses seem solid. Overall this is a well balanced paper which proposes a reasonable new idea, simple but effective, backed by sound theoretical analysis and well executed experimental evaluation.
assistant
This paper proposes a novel variant of Q-learning, called Maxmin Q-learning, to address the issue of overestimation bias Q-learning suffers from (variance of the reward of best action leading to overestimated reward). The idea is to keep a number of estimators each estimated using a different sub-sample, and taking the minimum of the (maximum) reward value of each. The paper gives theoretical analyses, in terms of the reduction in the overestimation bias, as well as the convergence of a class of generalized Q-learning methods including Maxmin Q-learning. The experiment section presents a thorough evaluation of the proposed method, including how the obtained rewards vary as a function of the variance of the reward function and as a function of learning steps, as compared to a number of existing methods such as the Double Q-learning method and its variants. The experimental results are quite convincing, and the theoretical analyses seem solid. Overall this is a well balanced paper which proposes a reasonable new idea, simple but effective, backed by sound theoretical analysis and well executed experimental evaluation.