Behavior Proximal Policy Optimization (2302.11312v1)

Abstract: Offline reinforcement learning (RL) is a challenging setting where existing off-policy actor-critic methods perform poorly due to the overestimation of out-of-distribution state-action pairs. Thus, various additional augmentations are proposed to keep the learned policy close to the offline dataset (or the behavior policy). In this work, starting from the analysis of offline monotonic policy improvement, we get a surprising finding that some online on-policy algorithms are naturally able to solve offline RL. Specifically, the inherent conservatism of these on-policy algorithms is exactly what the offline RL method needs to overcome the overestimation. Based on this, we propose Behavior Proximal Policy Optimization (BPPO), which solves offline RL without any extra constraint or regularization introduced compared to PPO. Extensive experiments on the D4RL benchmark indicate this extremely succinct method outperforms state-of-the-art offline RL algorithms. Our implementation is available at https://github.com/Dragon-Zhuang/BPPO.

Summary of "Behavior Proximal Policy Optimization"

The paper introduces Behavior Proximal Policy Optimization (BPPO), a novel approach to solving offline reinforcement learning (RL) tasks using the principles derived from on-policy algorithms. This method addresses the challenges of offline RL, specifically the overestimation of out-of-distribution (OOD) state-action pairs. BPPO leverages the inherent conservatism of on-policy algorithms, specifically Proximal Policy Optimization (PPO), to mitigate overestimation errors without the need for additional constraints or regularization.

Core Findings and Methodology

The paper begins with an exploration of offline RL challenges. Offline RL does not permit online interactions, relying solely on pre-existing datasets. Traditional methods often struggle with OOD state-action pairs, resulting in policy degradation. Most solutions focus on keeping policies close to the behavior policy, but they frequently involve complex augmentations.

BPPO is designed to naturally improve upon behavior policies by utilizing the monotonic policy improvement framework. This framework ensures that any new policy offers improved performance compared to the original behavior policy. The key insight is that algorithms traditionally used in online RL, like PPO, inherently possess features that make them suitable for offline RL environments. This includes a conservative nature that restricts learned policies from deviating significantly from known data distributions.

The theoretical underpinning involves applying the Performance Difference Theorem, which presents monotonic improvement conditions. BPPO optimizes expected advantages using importance sampling, adjusted with clipping techniques to manage policy divergence.

Numerical Results

The authors conduct extensive experiments on the D4RL benchmark, which includes a diverse set of environments such as Gym, Adroit, Kitchen, and Antmaze. BPPO demonstrates superior performance compared to state-of-the-art offline RL algorithms, highlighting its efficacy without complex policy constraints. In particular, BPPO achieves improved results on tasks with diverse challenging aspects, including tasks with sparse reward structures like Antmaze.

Implications and Future Developments

The primary contribution of BPPO lies in its simplicity and effectiveness; it achieves monotonic policy improvement in offline settings without relying on restrictive augmentations. This methodological minimalism opens pathways for further research, particularly in exploring the deeper implications of applying on-policy techniques to offline datasets.

The authors point out that BPPO's inherent simplicity suggests potential for expanding its application to broader RL domains. Future research might involve refining BPPO with alternative policy evaluation techniques or exploring its impact in environments with dynamic or rapidly changing state-action distributions.

Moreover, the clip ratio decay mechanism proven crucial in BPPO's success invites further investigation into adaptive clipping strategies for diverse RL scenarios. These adaptations could enhance policy stability and performance across various offline datasets.

Overall, BPPO exemplifies a promising direction for offline RL, challenging preconceived notions about the separation of online and offline methodologies and paving the way for more integrated and robust learning solutions in RL.