Semantic Reinforcement Learning with Action Grammars

The chapter discusses the concept of action grammars and their role in creating hierarchical reinforcement learning policies. It explores the difference between deterministic sequences and options in executing actions, as well as the connection between language and action in AI. The chapter also highlights the preference for classical algorithms and the evolution of grammar as the agent is trained.