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Pessimistic Auxiliary Policy for Offline Reinforcement Learning

arXiv:2602.23974v1 Announce Type: new Abstract: Offline reinforcement learning aims to learn an agent from pre-collected datasets, avoiding unsafe and inefficient real-time interaction. However, inevitable access to out-ofdistribution actions during the learning process introduces approximation errors, causing the error accumulation and considerable overestimation. In this paper, we construct a new pessimistic auxiliary policy for sampling reliable actions. Specifically, we develop a pessimistic auxiliary strategy by maximizing the lower confidence bound of the Q-function. The pessimistic auxiliary strategy exhibits a relatively high value and low uncertainty in the vicinity of the learned policy, avoiding the learned policy sampling high-value actions with potentially high errors during the learning process. Less approximation error introduced by sampled action from pessimistic auxiliary strategy leads to the alleviation of error accumulation. Extensive experiments on offline reinfor

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Fan Zhang, Baoru Huang, Xin Zhang · · 1 min read · 3 views
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arXiv:2602.23974v1 Announce Type: new Abstract: Offline reinforcement learning aims to learn an agent from pre-collected datasets, avoiding unsafe and inefficient real-time interaction. However, inevitable access to out-ofdistribution actions during the learning process introduces approximation errors, causing the error accumulation and considerable overestimation. In this paper, we construct a new pessimistic auxiliary policy for sampling reliable actions. Specifically, we develop a pessimistic auxiliary strategy by maximizing the lower confidence bound of the Q-function. The pessimistic auxiliary strategy exhibits a relatively high value and low uncertainty in the vicinity of the learned policy, avoiding the learned policy sampling high-value actions with potentially high errors during the learning process. Less approximation error introduced by sampled action from pessimistic auxiliary strategy leads to the alleviation of error accumulation. Extensive experiments on offline reinforcement learning benchmarks reveal that utilizing the pessimistic auxiliary strategy can effectively improve the efficacy of other offline RL approaches.

Executive Summary

This article proposes a pessimistic auxiliary policy for offline reinforcement learning, addressing the challenge of approximation errors caused by sampling out-of-distribution actions. The proposed strategy maximizes the lower confidence bound of the Q-function, resulting in a policy that exhibits high values and low uncertainty in the vicinity of the learned policy. This approach aims to alleviate error accumulation and improve the efficacy of other offline RL approaches. The authors conduct extensive experiments on offline reinforcement learning benchmarks, demonstrating the effectiveness of the proposed strategy. This research has implications for the development of more robust and efficient offline RL algorithms.

Key Points

  • ▸ The pessimistic auxiliary policy aims to alleviate approximation errors in offline reinforcement learning.
  • ▸ The proposed strategy maximizes the lower confidence bound of the Q-function.
  • ▸ The approach results in a policy that exhibits high values and low uncertainty in the vicinity of the learned policy.

Merits

Strength

The proposed strategy effectively addresses the challenge of approximation errors in offline reinforcement learning, leading to improved efficacy of other offline RL approaches.

Robustness

The pessimistic auxiliary policy exhibits high values and low uncertainty in the vicinity of the learned policy, making it more robust to approximation errors.

Efficiency

The approach aims to alleviate error accumulation, resulting in more efficient offline RL algorithms.

Demerits

Limitation

The proposed strategy may not be effective in scenarios where the lower confidence bound of the Q-function is not well-defined or is highly uncertain.

Overfitting

The pessimistic auxiliary policy may lead to overfitting if the learned policy is overly complex or has high capacity.

Computational Cost

The proposed strategy may require additional computational resources to maximize the lower confidence bound of the Q-function.

Expert Commentary

This article makes a significant contribution to the field of offline reinforcement learning by proposing a novel and effective strategy for addressing approximation errors. The pessimistic auxiliary policy is a well-motivated approach that leverages the lower confidence bound of the Q-function to improve policy performance. While the proposed strategy has several merits, including robustness and efficiency, it also has some limitations, such as potential overfitting and increased computational cost. Nevertheless, the results of the experiments demonstrate the effectiveness of the proposed strategy, and it is likely to have a significant impact on the development of more robust and efficient offline RL algorithms.

Recommendations

  • ✓ Future research should focus on extending the pessimistic auxiliary policy to more complex scenarios, such as multi-agent reinforcement learning or partial observability.
  • ✓ The proposed strategy should be applied to more real-world domains to evaluate its efficacy and robustness in practice.

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