Curiosity-Driven Exploration via Temporal Contrastive Learning

Faisal Mohamed, Catherine Ji, Benjamin Eysenbach, Glen Berseth

Keywords: reinforcement learning, exploration, intrinsic motivation, surprise, empowerment, contrastive learning

TL;DR: Temporal Contrastive learning can be used to derive exploration

Abstract: Effective exploration in reinforcement learning requires not only tracking where an agent has been, but also understanding how the agent perceives and represents the world. To learn powerful representations, an agent should actively explore states that facilitate a richer understanding of its environment. Temporal representations can capture the information necessary to solve a wide range of potential tasks like goal reaching and skill learning while avoiding the computational cost associated with full-state reconstruction. In this paper, we propose an exploration method that leverages temporal contrastive representations to guide exploration, aiming to maximize state coverage as perceived through the lens of these learned representations. We demonstrate that such representations can enable the learning of complex exploratory behaviors in locomotion, manipulation, and embodied-AI tasks, revealing previously inaccessible capabilities and behaviors that traditionally required extrinsic rewards.

Submission Number: 26