Energy-Based Predictive Representations for Partially Observed Reinforcement Learning

Abstract

In real-world applications, handling partial observability is a common requirement for reinforcement learning algorithms, which is not captured by a Markov decision process (MDP). Although partially observable Markov decision processes (POMDPs) have been specifically designed to address this requirement, they present significant computational and statistical challenges in learning and planning. In this work, we introduce the Energy-based Predictive Representation (EPR) to provide a unified approach for designing practical reinforcement learning algorithms in both the MDP and POMDP settings. This framework enables coherent handling of learning, exploration, and planning tasks. The proposed framework leverages a powerful neural energy-based model to extract an adequate representation, allowing for efficient approximation of Q-functions. This representation facilitates the efficient computation of confidence, enabling the implementation of optimism or pessimism in planning when faced with uncertainty. Consequently, it effectively manages the trade-off between exploration and exploitation. Experimental investigations demonstrate that the proposed algorithm achieves state-of-the-art performance in both MDP and POMDP settings.