Improving Intrinsic Exploration with Language Abstractions

Understand

Reinforcement learning (RL) agents are particularly hard to train when rewards are sparse.

• One common solution is to use intrinsic rewards to encourage agents to explore their environment.
• However, recent intrinsic exploration methods often use state-based novelty measures which reward low-level exploration and may not scale to domains requiring more abstract skills.
• Instead, we explore natural language as a general medium for highlighting relevant abstractions in an environment.