Loss is its own Reward: Self-Supervision for Reinforcement Learning

TL;DR

This paper proposes using self-supervised auxiliary tasks to enhance reinforcement learning by providing immediate supervision signals, improving data efficiency and policy performance especially when rewards are sparse or delayed.

Contribution

It introduces a framework for incorporating self-supervised tasks into reinforcement learning to improve learning efficiency and effectiveness.

Findings

Self-supervised auxiliary tasks improve data efficiency.

Pre-training with self-supervision enhances policy returns.

Auxiliary losses mitigate issues with sparse rewards.

Abstract

Reinforcement learning optimizes policies for expected cumulative reward. Need the supervision be so narrow? Reward is delayed and sparse for many tasks, making it a difficult and impoverished signal for end-to-end optimization. To augment reward, we consider a range of self-supervised tasks that incorporate states, actions, and successors to provide auxiliary losses. These losses offer ubiquitous and instantaneous supervision for representation learning even in the absence of reward. While current results show that learning from reward alone is feasible, pure reinforcement learning methods are constrained by computational and data efficiency issues that can be remedied by auxiliary losses. Self-supervised pre-training and joint optimization improve the data efficiency and policy returns of end-to-end reinforcement learning.