Dynamically Balanced Value Estimates for Actor-Critic Methods
Reinforcement learning in an actor-critic setting relies on accurate value estimates of the critic. However, the combination of function approximation, temporal difference (TD) learning and off-policy training can lead to an overestimating value function. A solution is to use Clipped Double Q-learning (CDQ), which is used in the TD3 algorithm and computes the minimum of two critics in the TD-target. We show that CDQ induces an underestimation bias and propose a new algorithm that accounts for this by using a weighted average of the target from CDQ and the target coming from a single critic. The weighting parameter is adjusted during training such that the value estimates match the actual discounted return on the most recent episodes and by that it balances over- and underestimation. Empirically, we obtain more accurate value estimates and demonstrate state of the art results on several OpenAI gym tasks.
This paper proposes the balanced clipped double estimator that uses a weighted averaged of the target from CDQ (leading to underestimations) and the target from a single critic (causing overestimations), to better estimate the Q value. The weight is adjusted by a simple heuristic during the training. Experimental results show that the BTD3 algorithm outperforms TD3, SAC, DDPG on six MuJoCo environments. How to get accurate function approximations is a very important problem. The idea that using a weight to balance the target value from CDQ and the target value from the single critic is welled motivated and interesting. Figure 1 gives a good motivation example to show how BCDQ reduces the estimator error of Q value. I appreciate that the authors plot the detail of beta in the training in Figure 2. The experimental results are somewhat weak as there are no results on Swimmer and HumanoidStandup. And I do not think BTD3 significantly outperforms other baselines in Ant, Walker2d, and Reacher. The paper would be more convincing with the result of BTD3 with fixed beta in Figure 1. Questions: 1. Are there any statistics of the estimation errors of TD3, DDPG, and BTD3 in all environments?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Dynamically Balanced Value Estimates for Actor-Critic Methods
Reinforcement learning in an actor-critic setting relies on accurate value estimates of the critic. However, the combination of function approximation, temporal difference (TD) learning and off-policy training can lead to an overestimating value function. A solution is to use Clipped Double Q-learning (CDQ), which is used in the TD3 algorithm and computes the minimum of two critics in the TD-target. We show that CDQ induces an underestimation bias and propose a new algorithm that accounts for this by using a weighted average of the target from CDQ and the target coming from a single critic. The weighting parameter is adjusted during training such that the value estimates match the actual discounted return on the most recent episodes and by that it balances over- and underestimation. Empirically, we obtain more accurate value estimates and demonstrate state of the art results on several OpenAI gym tasks.
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<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 the balanced clipped double estimator that uses a weighted averaged of the target from CDQ (leading to underestimations) and the target from a single critic (causing overestimations), to better estimate the Q value. The weight is adjusted by a simple heuristic during the training. Experimental results show that the BTD3 algorithm outperforms TD3, SAC, DDPG on six MuJoCo environments. How to get accurate function approximations is a very important problem. The idea that using a weight to balance the target value from CDQ and the target value from the single critic is welled motivated and interesting. Figure 1 gives a good motivation example to show how BCDQ reduces the estimator error of Q value. I appreciate that the authors plot the detail of beta in the training in Figure 2. The experimental results are somewhat weak as there are no results on Swimmer and HumanoidStandup. And I do not think BTD3 significantly outperforms other baselines in Ant, Walker2d, and Reacher. The paper would be more convincing with the result of BTD3 with fixed beta in Figure 1. Questions: 1. Are there any statistics of the estimation errors of TD3, DDPG, and BTD3 in all environments?
assistant
This paper proposes the balanced clipped double estimator that uses a weighted averaged of the target from CDQ (leading to underestimations) and the target from a single critic (causing overestimations), to better estimate the Q value. The weight is adjusted by a simple heuristic during the training. Experimental results show that the BTD3 algorithm outperforms TD3, SAC, DDPG on six MuJoCo environments. How to get accurate function approximations is a very important problem. The idea that using a weight to balance the target value from CDQ and the target value from the single critic is welled motivated and interesting. Figure 1 gives a good motivation example to show how BCDQ reduces the estimator error of Q value. I appreciate that the authors plot the detail of beta in the training in Figure 2. The experimental results are somewhat weak as there are no results on Swimmer and HumanoidStandup. And I do not think BTD3 significantly outperforms other baselines in Ant, Walker2d, and Reacher. The paper would be more convincing with the result of BTD3 with fixed beta in Figure 1. Questions: 1. Are there any statistics of the estimation errors of TD3, DDPG, and BTD3 in all environments?