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

2026-03-02

AI Summary

This work addresses the challenge of efficient exploration in reward-free reinforcement learning, where discovering informative states remains difficult. The authors propose a novel exploration method based on temporal contrastive representations that prioritizes visiting states which are hard to predict at future time steps, thereby encouraging the agent to actively seek out novel and informative experiences. By leveraging temporal structure alone—without relying on explicit distance metrics or episodic memory—the approach constructs a simple yet effective unsupervised representation to guide the acquisition of complex exploratory behaviors. Experimental results demonstrate that the proposed strategy successfully learns sophisticated behaviors in locomotion, manipulation, and embodied AI tasks—behaviors that typically require extrinsic rewards—significantly enhancing both the efficiency and capability of exploration in environments devoid of external rewards.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Temporal Planning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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 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. In this paper, we propose an exploration method that leverages temporal contrastive representations to guide exploration, prioritizing states with unpredictable future outcomes. We demonstrate that such representations can enable the learning of complex exploratory x in locomotion, manipulation, and embodied-AI tasks, revealing capabilities and behaviors that traditionally require extrinsic rewards. Unlike approaches that rely on explicit distance learning or episodic memory mechanisms (e.g., quasimetric-based methods), our method builds directly on temporal similarities, yielding a simpler yet effective strategy for exploration.

Problem

• reinforcement learning
• exploration
• temporal representations
• extrinsic rewards
• embodied AI

Innovation

• temporal contrastive representations
• reward-free exploration
• representation learning
• reinforcement learning
• embodied AI