Episodic Novelty Through Temporal Distance

2025-01-26

AI Summary

In sparse-reward Contextual Markov Decision Processes (CMDPs), state comparison is challenging and cross-episode exploration is inefficient. To address this, we propose a time-distance-based state similarity modeling method: for the first time, temporal distance is introduced as a robust, unsupervised intrinsic state metric; contrastive learning is employed to estimate temporal distances, circumventing the limitations of conventional counting-based methods and hand-crafted metrics. This yields a novel intrinsic reward mechanism that drives policies to efficiently identify novel states and enhances cross-task generalization. Evaluated on multiple sparse-reward CMDP benchmarks, our approach significantly outperforms state-of-the-art methods—achieving substantial improvements in both exploration efficiency and final task performance.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Exploration in sparse reward environments remains a significant challenge in reinforcement learning, particularly in Contextual Markov Decision Processes (CMDPs), where environments differ across episodes. Existing episodic intrinsic motivation methods for CMDPs primarily rely on count-based approaches, which are ineffective in large state spaces, or on similarity-based methods that lack appropriate metrics for state comparison. To address these shortcomings, we propose Episodic Novelty Through Temporal Distance (ETD), a novel approach that introduces temporal distance as a robust metric for state similarity and intrinsic reward computation. By employing contrastive learning, ETD accurately estimates temporal distances and derives intrinsic rewards based on the novelty of states within the current episode. Extensive experiments on various benchmark tasks demonstrate that ETD significantly outperforms state-of-the-art methods, highlighting its effectiveness in enhancing exploration in sparse reward CMDPs.

Problem

• Sparse Rewards
• Temporal State Changes
• Exploration Efficiency

Innovation

• ETD (Episodic Temporal Distance)
• Novelty Assessment
• Sparse Reward Environments