Batch Reinforcement Learning With A Nonparametric Off-policy Policy Gradient

Abstract

Off-policy Reinforcement Learning (RL) holds the promise of better data efficiency as it allows sample reuse and potentially enables safe interaction with the environment. Current off-policy policy gradient methods either suffer from high bias or high variance, delivering often unreliable estimates. The price of inefficiency becomes evident in real-world scenarios such as interaction-driven robot learning, where the success of RL has been rather limited, and a very high sample cost hinders straightforward application. In this paper, we propose a nonparametric Bellman equation, which can be solved in closed form. The solution is differentiable w.r.t the policy parameters and gives access to an estimation of the policy gradient. In this way, we avoid the high variance of importance sampling approaches, and the high bias of semi-gradient methods. We empirically analyze the quality of our gradient estimate against state-of-the-art methods, and show that it outperforms the baselines in term

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keytosatto2020batch

Related papers

• On-policy Policy Gradient Reinforcement Learning Without On-policy Sampling (2023)0.00
• Model-free Policy Learning With Reward Gradients (2021)0.00
• Policy Gradient For Reinforcement Learning With General Utilities (2022)0.00
• Off-policy Policy Gradient Algorithms By Constraining The State Distribution Shift (2019)0.00
• A Nearly Blackwell-optimal Policy Gradient Method (2021)0.00
• Smoothed Functional-based Gradient Algorithms For Off-policy Reinforcement Learning: A Non-asymptotic Viewpoint (2021)5.84
• Off-oab: Off-policy Policy Gradient Method With Optimal Action-dependent Baseline (2024)0.00
• Doubly Robust Off-policy Value And Gradient Estimation For Deterministic Policies (2020)0.00