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首页> 外文期刊>Mechatronics, IEEE/ASME Transactions on >Incremental Reinforcement Learning With Prioritized Sweeping for Dynamic Environments
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Incremental Reinforcement Learning With Prioritized Sweeping for Dynamic Environments

机译：用于动态环境优先扫描的增量强化学习

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摘要

In this paper, a novel incremental learning algorithm is presented for reinforcement learning (RL) in dynamic environments, where the rewards of state-action pairs may change over time. The proposed incremental RL (IRL) algorithm learns from the dynamic environments without making any assumptions or having any prior knowledge about the ever-changing environment. First, IRL generates a detector-agent to detect the changed part of the environment (drift environment) by executing a virtual RL process. Then, the agent gives priority to the drift environment and its neighbor environment for iteratively updating their state-action value functions using new rewards by dynamic programming. After the prioritized sweeping process, IRL restarts a canonical learning process to obtain a new optimal policy adapting to the new environment. The novelty is that IRL fuses the new information into the existing knowledge system incrementally as well as weakening the conflict between them. The IRL algorithm is compared to two direct approaches and various state-of-the-art transfer learning methods for classical maze navigation problems and an intelligent warehouse with multiple robots. The experimental results verify that IRL can effectively improve the adaptability and efficiency of RL algorithms in dynamic environments.
机译：在本文中，呈现了一种新的增量学习算法，用于在动态环境中的增强学习（RL），其中状态操作对的奖励可能随时间改变。所提出的增量RL（IRL）算法从动态环境中学习，而不会使任何假设或有任何关于不断变化的环境的先验知识。首先，iRR生成检测器代理通过执行虚拟RL处理来检测环境（漂移环境）的改变部分。然后，代理优先考虑漂移环境及其邻居环境，用于使用动态编程使用新奖励来迭代地更新其状态动作值函数。在优先顺序的Sweeping进程之后，IRL重新启动规范学习过程，以获得适应新环境的新的最佳政策。新颖性是IRL逐步使新信息融入现有的知识系统，以及削弱它们之间的冲突。将IRL算法与两种直接方法和各种最先进的传输学习方法进行比较，用于古典迷宫导航问题和具有多个机器人的智能仓库。实验结果验证了IRL可以有效提高动态环境中R1算法的适应性和效率。

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