Proximal Reinforcement Learning: A New Theory of Sequential Decision Making in Primal-Dual Spaces

TL;DR

This paper introduces proximal reinforcement learning, a new theoretical framework using primal-dual spaces and proximal operators to develop reliable, safe, and stable RL algorithms with convergence guarantees.

Contribution

It presents a novel proximal operator-based framework that unifies and generalizes existing RL algorithms, enabling stable off-policy learning and integration with stochastic optimization.

Findings

Provides a rigorous mathematical foundation for RL algorithms.

Develops operator splitting methods for safe gradient decomposition.

Shows connections between natural gradient, mirror descent, and proximal methods.

Abstract

In this paper, we set forth a new vision of reinforcement learning developed by us over the past few years, one that yields mathematically rigorous solutions to longstanding important questions that have remained unresolved: (i) how to design reliable, convergent, and robust reinforcement learning algorithms (ii) how to guarantee that reinforcement learning satisfies pre-specified "safety" guarantees, and remains in a stable region of the parameter space (iii) how to design "off-policy" temporal difference learning algorithms in a reliable and stable manner, and finally (iv) how to integrate the study of reinforcement learning into the rich theory of stochastic optimization. In this paper, we provide detailed answers to all these questions using the powerful framework of proximal operators. The key idea that emerges is the use of primal dual spaces connected through the use of a Legendre transform. This allows temporal difference updates to occur in dual spaces, allowing a variety of important technical advantages. The Legendre transform elegantly generalizes past algorithms for solving reinforcement learning problems, such as natural gradient methods, which we show relate closely to the previously unconnected framework of mirror descent methods. Equally importantly, proximal operator theory enables the systematic development of operator splitting methods that show how to safely and reliably decompose complex products of gradients that occur in recent variants of gradient-based temporal difference learning. This key technical innovation makes it possible to finally design "true" stochastic gradient methods for reinforcement learning. Finally, Legendre transforms enable a variety of other benefits, including modeling sparsity and domain geometry. Our work builds extensively on recent work on the convergence of saddle-point algorithms, and on the theory of monotone operators.