论文标题
通过Q-Network表示形式转移强化学习的强化学习
Transfer Reinforcement Learning for Differing Action Spaces via Q-Network Representations
论文作者
论文摘要
加强学习中的转移学习方法旨在通过利用接受过在类似源域进行培训的其他代理商中学到的知识来帮助代理学习其目标领域。例如，该空间内的最新研究重点已放在具有不同过渡动态和奖励功能的任务之间的知识转移上。但是，几乎没有重点放在具有不同动作空间的任务之间的知识转移上。在本文中，我们将处理在动作空间不同的域之间转移学习的任务。我们提出了一种基于源嵌入相似性的奖励成型方法，该方法适用于具有离散和连续作用空间的域。我们的方法的功效在转移到Acrobot-V1和Pendulum-V0域中的受限作用空间时进行了评估。与两个基准的比较表明，在这些连续的动作空间中，我们的方法并不能优于这些基准，但在这些离散的作用空间中确实显示出改善。我们以这项工作的未来方向结束了分析。
Transfer learning approaches in reinforcement learning aim to assist agents in learning their target domains by leveraging the knowledge learned from other agents that have been trained on similar source domains. For example, recent research focus within this space has been placed on knowledge transfer between tasks that have different transition dynamics and reward functions; however, little focus has been placed on knowledge transfer between tasks that have different action spaces. In this paper, we approach the task of transfer learning between domains that differ in action spaces. We present a reward shaping method based on source embedding similarity that is applicable to domains with both discrete and continuous action spaces. The efficacy of our approach is evaluated on transfer to restricted action spaces in the Acrobot-v1 and Pendulum-v0 domains. A comparison with two baselines shows that our method does not outperform these baselines in these continuous action spaces but does show an improvement in these discrete action spaces. We conclude our analysis with future directions for this work.
