Temporal Representations for Exploration: Learning Complex Exploratory Behavior without Extrinsic Rewards

ArXi:2603.02008v2 Announce Type: replace 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 contribute to its knowledge of the environment. Temporal representations can capture the information necessary to solve a wide range of potential tasks while avoiding the computational cost associated with full state reconstruction.