Influencing Reinforcement Learning through Natural Language Guidance

Understand

Interactive reinforcement learning agents use human feedback or instruction to help them learn in complex environments.

• Often, this feedback comes in the form of a discrete signal that is either positive or negative.
• While informative, this information can be difficult to generalize on its own.
• In this work, we explore how natural language advice can be used to provide a richer feedback signal to a reinforcement learning agent by extending policy shaping, a well-known Interactive reinforcement learning technique.