Skill Transfer in Deep Reinforcement Learning under Morphological Heterogeneity

Understand

Transfer learning methods for reinforcement learning (RL) domains facilitate the acquisition of new skills using previously acquired knowledge.

• The vast majority of existing approaches assume that the agents have the same design, e.g.
• same shape and action spaces.
• In this paper we address the problem of transferring previously acquired skills amongst morphologically different agents (MDAs).