  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.
