On-Policy Policy Gradient Reinforcement Learning Without On-Policy Sampling

ICLR 2025 Conference Submission291 Authors

Keywords: reinforcement learning, on-policy, policy gradient, data collection

TL;DR: We introduce a non-i.i.d., off-policy sampling method to produce data that more closely matches the expected on-policy data distribution than on-policy sampling can produce, thus improving the data efficiency of on-policy policy gradient algorithms.

Abstract: On-policy reinforcement learning RL algorithms perform policy updates using i.i.d. trajectories collected by the current policy. However, after observing only a finite number of trajectories, on-policy sampling may produce data that fails to match the expected on-policy data distribution. This sampling error leads to noisy updates and data inefficient on-policy learning. Recent work in the policy evaluation setting has shown that non-i.i.d., off-policy sampling can produce data with lower sampling error than on-policy sampling can produce~\citep{zhong2022robust}. Motivated by this observation, we introduce an adaptive, off-policy sampling method to improve the data efficiency of on-policy policy gradient algorithms. Our method, Proximal Robust On-Policy Sampling (PROPS) reduces sampling error by collecting data with a behavior policy that increases the probability of sampling actions that are under-sampled with respect to the current policy. Rather than discarding data from old policies -- as is commonly done in on-policy algorithms -- PROPS uses data collection to adjust the distribution of previously collected data to be approximately on-policy. We empirically evaluate PROPS on both continuous-action MuJoCo benchmark tasks as well discrete-action tasks and demonstrate that (1) PROPS decreases sampling error throughout training and (2) improves the data efficiency of on-policy policy gradient algorithms. Our work improves the RL community’s understanding of a nuance in the on-policy vs off-policy dichotomy: on-policy learning requires on-policy data, not on-policy sampling.

Primary Area: reinforcement learning

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Submission Number: 291