Behavior Proximal Policy Optimization
This addresses the problem of overestimation in offline RL for researchers and practitioners, offering a simpler and effective solution, though it is incremental as it builds on existing PPO methods.
The paper tackles offline reinforcement learning by proposing Behavior Proximal Policy Optimization (BPPO), which adapts an online on-policy algorithm to avoid overestimation issues without extra constraints, and it outperforms state-of-the-art methods on the D4RL benchmark.
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.