Epistemically-guided forward-backward exploration

2025-07-07

AI Summary

This work addresses the reward-free exploration challenge in zero-shot reinforcement learning. We propose a cognitive uncertainty-driven exploration method grounded in forward-backward (FB) representation learning. Our core contribution is the first deep integration of FB representation learning with exploration policy optimization: by performing Bayesian inference to minimize the posterior variance of the learned representations, we directly reduce epistemic uncertainty, thereby guiding efficient and targeted data collection. The method decomposes the state occupancy measure via FB factorization and jointly optimizes representation learning and exploratory behavior. Empirical results demonstrate that our approach substantially reduces the sample complexity of FB-based algorithms, outperforms existing exploration baselines across multiple zero-shot tasks, and improves both policy learning efficiency and generalization performance.

Technology Category

• Reasoning under Uncertainty: Uncertainty Representations
• Search and Optimization: Learning to Search
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Zero-shot reinforcement learning is necessary for extracting optimal policies in absence of concrete rewards for fast adaptation to future problem settings. Forward-backward representations (FB) have emerged as a promising method for learning optimal policies in absence of rewards via a factorization of the policy occupancy measure. However, up until now, FB and many similar zero-shot reinforcement learning algorithms have been decoupled from the exploration problem, generally relying on other exploration algorithms for data collection. We argue that FB representations should fundamentally be used for exploration in order to learn more efficiently. With this goal in mind, we design exploration policies that arise naturally from the FB representation that minimize the posterior variance of the FB representation, hence minimizing its epistemic uncertainty. We empirically demonstrate that such principled exploration strategies improve sample complexity of the FB algorithm considerably in comparison to other exploration methods. Code is publicly available at https://sites.google.com/view/fbee-url.

Problem

• Zero-shot reinforcement learning without concrete rewards
• Forward-backward representations for policy optimization
• Exploration policies minimizing epistemic uncertainty

Innovation

• Epistemically-guided forward-backward exploration strategy
• Minimizing posterior variance of FB representation
• Improving sample complexity via principled exploration