WHEN DATA GEOMETRY MEETS DEEP FUNCTION: GENERALIZING OFFLINE REINFORCEMENT LEARNING

Abstract

In offline reinforcement learning (RL), one detrimental issue to policy learning is the error accumulation of deep Q function in out-of-distribution (OOD) areas. Unfortunately, existing offline RL methods are often over-conservative, inevitably hurting generalization performance outside data distribution. In our study, one interesting observation is that deep Q functions approximate well inside the convex hull of training data. Inspired by this, we propose a new method, DOGE (Distance-sensitive Offline RL with better GEneralization). DOGE marries dataset geometry with deep function approximators in offline RL, and enables exploitation in generalizable OOD areas rather than strictly constraining policy within data distribution. Specifically, DOGE trains a state-conditioned distance function that can be readily plugged into standard actor-critic methods as a policy constraint. Simple yet elegant, our algorithm enjoys better generalization compared to state-of-the-art methods on D4RL benchmarks. Theoretical analysis demonstrates the superiority of our approach to existing methods that are solely based on data distribution or support constraints.

1. INTRODUCTION

Offline reinforcement learning (RL) provides a new possibility to learn optimized policies from large, pre-collected datasets without any environment interaction (Levine et al., 2020) . This holds great promise to solve many real-world problems when online interaction is costly or dangerous yet historical data is easily accessible (Zhan et al., 2022) . However, the optimization nature of RL, as well as the need for counterfactual reasoning on unseen data under offline setting, have caused great technical challenges for designing effective offline RL algorithms. Evaluating value function outside data coverage areas can produce falsely optimistic values; without corrective information from online interaction, such estimation errors can accumulate quickly and misguide policy learning process (Van Hasselt et al., 2018; Fujimoto et al., 2018; Kumar et al., 2019) . Recent model-free offline RL methods investigate this error accumulation challenge in several ways: 1) Policy Constraint: directly constraining learned policy to stay inside distribution, or with the support of dataset (Kumar et al., 2019) ; 2) Value Regularization: regularizing value function to assign low values at out-of-distribution (OOD) actions (Kumar et al., 2020b) ; 3) In-sample Learning: learning value function within data samples (Kostrikov et al., 2021b) or simply treating it as the value function of behavioral policy (Brandfonbrener et al., 2021) . All three schools of methods share similar traits of being conservative and omitting evaluation on OOD data, which brings benefits of minimizing model exploitation error, but at the expense of poor generalization of learned policy in OOD regions. Thus, a gaping gap still exists when such methods are applied to real-world tasks, where most datasets only partially cover state-action space with suboptimal policies. Meanwhile, online deep reinforcement learning (DRL) that leverages powerful deep neural network (DNN) with optimistic exploration on unseen samples can yield high-performing policies with promising generalization performance (Mnih et al., 2015; Silver et al., 2017; Degrave et al., 2022;  * Corresponding authors 1

availability

//github.com/Facebear-ljx/DOGE

