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.

本研究旨在探究在不同动作空间领域之间进行知识传递的可能性和有效性，提出了一种基于源嵌入相似性的奖励塑形方法，可适用于具有离散和连续动作空间的领域。在 Acrobot-v1 和 Pendulum-v0 领域上，基于两个基线的比较表明我们的方法没有在连续动作空间中取得更好的结果，但在离散动作空间中确实表现出了改进。