Dual RL: Unification and New Methods for Reinforcement and Imitation Learning (2302.08560v3)

Abstract: The goal of reinforcement learning (RL) is to find a policy that maximizes the expected cumulative return. It has been shown that this objective can be represented as an optimization problem of state-action visitation distribution under linear constraints. The dual problem of this formulation, which we refer to as dual RL, is unconstrained and easier to optimize. In this work, we first cast several state-of-the-art offline RL and offline imitation learning (IL) algorithms as instances of dual RL approaches with shared structures. Such unification allows us to identify the root cause of the shortcomings of prior methods. For offline IL, our analysis shows that prior methods are based on a restrictive coverage assumption that greatly limits their performance in practice. To fix this limitation, we propose a new discriminator-free method ReCOIL that learns to imitate from arbitrary off-policy data to obtain near-expert performance. For offline RL, our analysis frames a recent offline RL method XQL in the dual framework, and we further propose a new method f-DVL that provides alternative choices to the Gumbel regression loss that fixes the known training instability issue of XQL. The performance improvements by both of our proposed methods, ReCOIL and f-DVL, in IL and RL are validated on an extensive suite of simulated robot locomotion and manipulation tasks. Project code and details can be found at this https://hari-sikchi.github.io/dual-rl.

• The paper presents a unified dual formulation that recasts reinforcement learning, allowing integration of RL and IL algorithms through dual-Q and dual-V approaches.
• The paper reformulates existing methods such as XQL and IQLearn within its dual framework, highlighting limitations from coverage assumptions in expert and suboptimal data.
• The paper proposes novel algorithms, ReCOIL and f-DVL, which enhance performance and training stability in simulated robotic locomotion and manipulation tasks.

An Examination of "Dual RL: Unification and New Methods for Reinforcement and Imitation Learning"

The paper "Dual RL: Unification and New Methods for Reinforcement and Imitation Learning" addresses a pertinent challenge in the field of reinforcement learning (RL) and imitation learning (IL): the need to develop a unified framework that facilitates better understanding and extension of existing algorithms. The work introduces a duality-based perspective, building on existing RL paradigms, to propose new algorithms that overcome some of the intrinsic limitations observed in prior approaches.

Core Contributions

1. Unification through Duality:

The authors propose a dual formulation of the standard RL objective, which traditionally aims to maximize expected cumulative return. By recasting this as a dual problem involving the state-action visitation distribution under specific linear constraints, they present a unified perspective on various RL and IL algorithms. This framework, referred to as Dual RL, facilitates the identification of shared structures and limitations across a spectrum of state-of-the-art offline RL and offline IL methods. The paper delineates this dual RL approach into two main formulations: dual-Q and dual-V, which represent two different ways of addressing the linear constraints accompanying the RL objective.

2. Analysis of Existing Methods:

Through comprehensive theoretical and empirical analysis, the authors cast several notable offline RL and IL algorithms such as XQL and IQLearn into the dual RL framework. This illustration uncovers the restrictive coverage assumptions prevalent in many IL approaches. Particularly, they emphasize the limitation posed by these assumptions in estimating density ratios accurately when expert and suboptimal data do not share substantial overlap.

3. Proposal of ReCOIL and $f$-DVL:

To counteract the limitations identified in existing IL methods, they propose ReCOIL, which eschews the dependency on discriminators and restrictive assumptions by formulating an alternative mixture distribution approach. In offline RL, the instability of the XQL method—owing to the Gumbel regression clay in handling BeLLMan errors—is tackled by introducing $f$-DVL. This new family of algorithms leverages different $f$-divergences to stabilize training and optimize performance.

Numerical Results and Implications

The methods proposed, ReCOIL and $f$-DVL, are rigorously validated through benchmarks on a suite of simulated robotic locomotion and manipulation tasks. The empirical results denote significant performance gains, with ReCOIL demonstrating substantial enhancements over IL baselines by effectively utilizing arbitrary off-policy data. Similarly, $f$-DVL exhibits superior training stability and efficacy in addressing the value overestimation issues inherent in previous RL algorithms.

Theoretical and Practical Implications

The theoretical contributions of this work lie in extending the understanding of duality in reinforcement and imitation learning, highlighting the common underpinnings and potential avenues of integration across diverse algorithmic strategies. Practically, these insights offer pathways to designing more robust agents capable of leveraging available data more effectively in both offline and online contexts.

Future Directions

This paper opens up compelling prospects for further investigation. The dual RL framework could aid in developing novel algorithms that navigate the RL problem space with more nuanced understanding and utilize non-parametric techniques for distribution matching. Further, extending the dual RL approach to on-policy methods and exploring its synergistic benefits with other learning paradigms such as unsupervised or meta-learning could catalyze significant advancements in the field.

In summary, the paper presents a substantive advancement in formalizing a unifying framework through dual RL, offering tools that not only refine current practices but potentially invigorate the future trajectory of RL and IL research.