在连续控制强化学习任务的截断双 Q 学习中利用估计偏差
Exploiting Estimation Bias in Clipped Double Q-Learning for Continous Control Reinforcement Learning Tasks
AI总结：
本文提出一种偏差利用（BE）机制，在Actor-Critic方法的深度双Q学习中动态选择最有利的估计偏差，以解决连续控制强化学习中的次优策略问题，且不增加计算复杂度。
摘要：
连续控制深度强化学习（RL）方法已知会受到估计偏差的影响，导致策略次优。本文引入了创新的RL方法，重点解决并利用Actor-Critic方法在连续控制任务中的估计偏差，使用深度双Q学习。我们设计了一种偏差利用（BE）机制，在RL智能体训练期间动态选择最有利的估计偏差。大多数最先进的深度RL算法都可以配备BE机制，而不会阻碍性能或增加计算复杂度。我们在各种连续控制任务上的广泛实验证明了我们方法的有效性。我们表明，配备此方法的RL算法可以匹配或超越其对应算法，特别是在估计偏差显著影响学习的环境中。结果强调了偏差利用在改进RL策略学习中的重要性。
英文摘要：
Continuous control Deep Reinforcement Learning (RL) approaches are known to suffer from estimation biases, leading to suboptimal policies. This paper introduces innovative methods in RL, focusing on addressing and exploiting estimation biases in Actor-Critic methods for continuous control tasks, using Deep Double Q-Learning. We design a Bias Exploiting (BE) mechanism to dynamically select the most advantageous estimation bias during training of the RL agent. Most State-of-the-art Deep RL algorithms can be equipped with the BE mechanism, without hindering performance or computational complexity. Our extensive experiments across various continuous control tasks demonstrate the effectiveness of our approaches. We show that RL algorithms equipped with this method can match or surpass their counterparts, particularly in environments where estimation biases significantly impact learning. The results underline the importance of bias exploitation in improving policy learning in RL.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
