Using Natural Language for Reward Shaping in Reinforcement Learning

Understand

Recent reinforcement learning (RL) approaches have shown strong performance in complex domains such as Atari games, but are often highly sample inefficient.

• A common approach to reduce interaction time with the environment is to use reward shaping, which involves carefully designing reward functions that provide the agent intermediate rewards for progress towards the goal.
• However, designing appropriate shaping rewards is known to be difficult as well as time-consuming.
• In this work, we address this problem by using natural language instructions to perform reward shaping.