Skill-Based Reinforcement Learning with Intrinsic Reward Matching

Ademi Adeniji, Amber Xie, Pieter Abbeel

Keywords: Unsupervised Reinforcement Learning, Reinforcement Learning, Deep Learning

TL;DR: We unify unsupervised RL skill pretraining and downstream finetuning phases of learning by leveraging the skill discriminator as a task specifier.

Abstract: While unsupervised skill discovery has shown promise in autonomously acquiring behavioral primitives, there is still a large methodological disconnect between task-agnostic skill pretraining and downstream, task-aware finetuning. We present Intrinsic Reward Matching (IRM), which unifies these two phases of learning via the $\textit{skill discriminator}$, a pretraining model component often discarded during finetuning. Conventional approaches finetune pretrained agents directly at the policy level, often relying on expensive environment rollouts to empirically determine the optimal skill. However, often the most concise yet complete description of a task is the reward function itself, and skill learning methods learn an $\textit{intrinsic}$ reward function via the discriminator that corresponds to the skill policy. We propose to leverage the skill discriminator to $\textit{match}$ the intrinsic and downstream task rewards and determine the optimal skill for an unseen task without environment samples, consequently finetuning with greater sample-efficiency. Furthermore, we generalize IRM to sequence skills and solve more complex, long-horizon tasks. We demonstrate that IRM is competitive with previous skill selection methods on the Unsupervised Reinforcement Learning Benchmark and enables us to utilize pretrained skills far more effectively on challenging tabletop manipulation tasks.

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