Bridging State and History Representations: Understanding Self-Predictive RL

The Unified View of State and History Representations in Self-Predictive RL

Introduction

Reinforcement learning (RL) has demonstrated considerable success in learning optimal policies via direct interactions with the environment. A key challenge, however, arises when faced with high-dimensional or noisy observations, which become even more pronounced in partially observable settings. To combat this, the compression of observations into a latent state space as state and history abstractions has been a focal area of research. These methods are often based on a myriad of diverse representation learning techniques, which can lead to a decision-making conundrum for RL practitioners seeking the best approach for their specific problems.

Self-Predictive Representations in RL

Recent research has emphasized understanding the essence of effective representations—the properties they should hold and the learning procedures that can acquire them. Self-predictive representations are a compelling case, embodying the idea that an effective encoder should be able to predict its future latent states from current states or histories. Various methods and theoretical frameworks have been proposed for learning such representations, yet their interconnections have remained opaque.

This paper contributes to the reconciliation of these seemingly distinct methods by establishing the relatedness of their underlying objectives. These methods share a core property: they revolve around the self-predictive condition, enabling them to drive the evolution of latent representations. Specifically, the encoder can predict subsequent states using current observations within the RL framework, thereby enabling the planning or behaving directly within the latent space.

A thorough examination of the underlying algorithms has revealed that this unifying principle is manifested in various forms across the literature—whether it's through the acquisition of Q-irrelevance abstraction, model-irrelevance abstraction, or embracement of a variety of facets encapsulated within different theoretical propositions. Notably, many of these frameworks converge upon conditions that are closely interconnected and, to some extent, sequentially imply one another, which subsequently led to the understanding that learning self-predictive representations relates to broader concepts like bisimulation and information states.

Algorithmic Insights

The theoretical exploration of these representations and the conditions they satisfy has birthed a minimalist RL algorithm designed to learn self-predictive representations in an end-to-end fashion. This algorithm diverges from the current trend involving complex components like reward models and multi-step predictions. It introduces a single auxiliary loss and leverages stop-gradient techniques to circumvent representational collapse in POMDPs.

Critically, the algorithm enables empirical investigations into the distinct impact of representation learning without the conventional entanglements with policy optimization. In contrast to prior art, the algorithm creates an opportunity for systematically understanding the role representation learning plays in isolation, thus providing a purified lens through which the RL community can re-examine established techniques and potentially align them with the task at hand more effectively.

Empirical Validation and Practical Recommendations

The empirical evaluation has validated most theoretical predictions, substantiating the minimalist algorithm's equivalence or superiority to more complex counterparts in specific benchmarks. This not only unearths the potential of end-to-end learning but also the significance of choosing the correct objective and optimization dynamics.

The evidence gathered also provides guidance for practitioners, emphasizing task analysis before selecting representation learning strategies and proposing the minimalist algorithm as a baseline. The differential impacts of learning objectives were also highlighted, with practitioners being advised to account for task specificity, particularly when dealing with noise and distractions, and embracing end-to-end learning with model-free RL for policy optimization.

In conclusion, the present work offers a cohesive perspective on state and history representations for self-predictive RL, encompassing theoretical insights and algorithmic simplicity that together propel forward our comprehension of critical facets in solving RL problems under partial observability.