The ability of an algorithm to learn only from rewards is a very important characteristic that led us to develop reinforcement learning algorithms. This enables an agent to learn and improve its policy from scratch without additional supervision. Despite this, there are situations where other expert agents are already employed in a given environment. Imitation learning (IL) algorithms leverage the expert by imitating their actions and learning the policy from them.
This chapter focuses on imitation learning. Although different to reinforcement learning, imitation learning offers great opportunities and capabilities, especially in environments with very large state spaces and sparse rewards. Obviously, imitation learning is possible only when a more expert agent to imitate is available.
The chapter will focus on the main concepts and...
