Teachable Reinforcement Learning via Advice Distillation

Part of Advances in Neural Information Processing Systems 34 pre-proceedings (NeurIPS 2021)


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Olivia Watkins, Abhishek Gupta, Trevor Darrell, Pieter Abbeel, Jacob Andreas


Training automated agents to perform complex behaviors in interactive environments is challenging: reinforcement learning requires careful hand-engineering of reward functions, imitation learning requires specialized infrastructure and access to a human expert, and learning from intermediate forms of supervision (like binary preferences) is time-consuming and provides minimal information per human intervention. Can we overcome these challenges by building agents that learn from rich, interactive feedback? We propose a new supervision paradigm for interactive learning based on teachable decision-making systems, which learn from structured advice provided by an external teacher. We begin by introducing a class of human-in-the-loop decision making problems in which different forms of human provided advice signals are available to the agent to guide learning. We then describe a simple policy learning algorithm that first learns to interpret advice, then learns from advice to target tasks in the absence of human supervision. In puzzle-solving, navigation, and locomotion domains, we show that agents that learn from advice can acquire new skills with significantly less human supervision required than standard reinforcement or imitation learning systems.