标题： 具有连续控制的强化学习中的增强回放记忆 显示英文标题

标题： Augmented Replay Memory in Reinforcement Learning With Continuous Control

摘要： 目前，在线强化学习代理能够通过将其转换为更高阶的价值函数来处理越来越多的数据。 从环境中收集信息的这种扩展增加了代理的状态空间，使其能够扩展到更复杂的问题，但也增加了由于在冗余或冲突的数据上学习而导致遗忘的风险。 为了改善大量数据的逼近，通常在每个学习步骤中都会重新播放存储在回放内存缓冲区中的过去经验的一个随机小批量数据。 这项研究工作受到了一种生物机制的启发，该机制作为保护人类大脑高级认知功能的一层：主动记忆巩固通过动态处理新信息来减轻先前记忆的遗忘效应。 所提出的增强型记忆回放（AMR）通过改变或增强记忆的相关性，实现了类似的动力学，从而优化了代理记忆结构中经验的回放。 实验结果表明，能够增加特定记忆显著性的进化AMR增强函数可以进一步提高处理连续动作域复杂性的学习算法的稳定性和收敛速度。 显示英文摘要

摘要： Online reinforcement learning agents are currently able to process an increasing amount of data by converting it into a higher order value functions. This expansion of the information collected from the environment increases the agent's state space enabling it to scale up to a more complex problems but also increases the risk of forgetting by learning on redundant or conflicting data. To improve the approximation of a large amount of data, a random mini-batch of the past experiences that are stored in the replay memory buffer is often replayed at each learning step. The proposed work takes inspiration from a biological mechanism which act as a protective layer of human brain higher cognitive functions: active memory consolidation mitigates the effect of forgetting of previous memories by dynamically processing the new ones. The similar dynamics are implemented by a proposed augmented memory replay AMR capable of optimizing the replay of the experiences from the agent's memory structure by altering or augmenting their relevance. Experimental results show that an evolved AMR augmentation function capable of increasing the significance of the specific memories is able to further increase the stability and convergence speed of the learning algorithms dealing with the complexity of continuous action domains.