Guiding Reinforcement Learning Exploration Using Natural Language

Understand

In this work we present a technique to use natural language to help reinforcement learning generalize to unseen environments.

• This technique uses neural machine translation, specifically the use of encoder-decoder networks, to learn associations between natural language behavior descriptions and state-action information.
• We then use this learned model to guide agent exploration using a modified version of policy shaping to make it more effective at learning in unseen environments.
• We evaluate this technique using the popular arcade game, Frogger, under ideal and non-ideal conditions.