论文标题
通过使用代理的生态系统来改善对新环境的概括，并消除增强学习中的灾难性遗忘
Improving generalization to new environments and removing catastrophic forgetting in Reinforcement Learning by using an eco-system of agents
论文作者
论文摘要
将加固学习（RL）代理适应看不见的环境是一项艰巨的任务，这是一项艰巨的任务，因为在训练环境中典型的过度拟合。 RL代理通常能够求解非常接近训练的环境的环境，但是当环境变得明显不同时，其性能会迅速下降。当代理在新环境上进行重新训练时，就会出现第二个问题：存在灾难性遗忘的风险，在此期间，以前看到的环境的性能受到严重阻碍。本文提出了一种新颖的方法，该方法利用了代理商的生态系统来解决这两种问题。因此，收集了（有限的）自适应能力，以建立一个高度适应性的生态系统。
Adapting a Reinforcement Learning (RL) agent to an unseen environment is a difficult task due to typical over-fitting on the training environment. RL agents are often capable of solving environments very close to the trained environment, but when environments become substantially different, their performance quickly drops. When agents are retrained on new environments, a second issue arises: there is a risk of catastrophic forgetting, where the performance on previously seen environments is seriously hampered. This paper proposes a novel approach that exploits an eco-system of agents to address both concerns. Hereby, the (limited) adaptive power of individual agents is harvested to build a highly adaptive eco-system.
