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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Incremental Reinforcement Learning in Continuous Spaces via Policy Relaxation and Importance Weighting
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Incremental Reinforcement Learning in Continuous Spaces via Policy Relaxation and Importance Weighting

机译：通过政策放松和重要加权在连续空间中增量加强学习

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摘要

In this paper, a systematic incremental learning method is presented for reinforcement learning in continuous spaces where the learning environment is dynamic. The goal is to adjust the previously learned policy in the original environment to a new one incrementally whenever the environment changes. To improve the adaptability to the ever-changing environment, we propose a two-step solution incorporated with the incremental learning procedure: policy relaxation and importance weighting. First, the behavior policy is relaxed to a random one in the initial learning episodes to encourage a proper exploration in the new environment. It alleviates the conflict between the new information and the existing knowledge for a better adaptation in the long term. Second, it is observed that episodes receiving higher returns are more in line with the new environment, and hence contain more new information. During parameter updating, we assign higher importance weights to the learning episodes that contain more new information, thus encouraging the previous optimal policy to be faster adapted to a new one that fits in the new environment. Empirical studies on continuous controlling tasks with varying configurations verify that the proposed method achieves a significantly faster adaptation to various dynamic environments than the baselines.
机译：在本文中，提出了一种用于在学习环境是动态的连续空间中的加强学习的系统增量学习方法。目标是在环境变化时逐步调整原始环境中的先前学习的策略到新的策略。为了提高对不断变化的环境的适应性，我们提出了一种与增量学习程序的两步解决方案：政策放松和重量。首先，行为政策在初始学习剧集中放宽一个随机的一个，以鼓励在新环境中进行适当的探索。它减轻了新信息与现有知识之间的冲突，以便长期更好地适应。其次，观察到，接收更高返回的剧集更加符合新环境，因此包含更多的新信息。在参数更新期间，我们为包含更多新信息的学习剧集分配更高的重量，从而鼓励以前的最佳政策更快地适应适合新环境的新型。与不同的配置连续控制任务的实证研究验证，该方法实现了显著更快地适应各种动态环境比基线。

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