CAWR: Corruption-Averse Advantage-Weighted Regression for Robust Policy Optimization

• URL: http://arxiv.org/abs/2506.15654v1
• Date: Wed, 18 Jun 2025 17:31:26 GMT
• Title: CAWR: Corruption-Averse Advantage-Weighted Regression for Robust Policy Optimization
• Authors: Ranting Hu,
• Abstract summary: This paper focuses on the potential for learning over-conservative policies due to data corruption.<n>We propose Corruption-Averse Advantage-Weighted Regression (CAWR), which incorporates a set of robust loss functions during policy optimization.<n> Numerical experiments on the D4RL benchmark show that our method can learn superior policies from suboptimal offline data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Offline reinforcement learning (offline RL) algorithms often require additional constraints or penalty terms to address distribution shift issues, such as adding implicit or explicit policy constraints during policy optimization to reduce the estimation bias of functions. This paper focuses on a limitation of the Advantage-Weighted Regression family (AWRs), i.e., the potential for learning over-conservative policies due to data corruption, specifically the poor explorations in suboptimal offline data. We study it from two perspectives: (1) how poor explorations impact the theoretically optimal policy based on KL divergence, and (2) how such poor explorations affect the approximation of the theoretically optimal policy. We prove that such over-conservatism is mainly caused by the sensitivity of the loss function for policy optimization to poor explorations, and the proportion of poor explorations in offline datasets. To address this concern, we propose Corruption-Averse Advantage-Weighted Regression (CAWR), which incorporates a set of robust loss functions during policy optimization and an advantage-based prioritized experience replay method to filter out poor explorations. Numerical experiments on the D4RL benchmark show that our method can learn superior policies from suboptimal offline data, significantly enhancing the performance of policy optimization.

Related papers

• EXPO: Stable Reinforcement Learning with Expressive Policies [74.30151915786233]
  We propose a sample-efficient online reinforcement learning algorithm to maximize value with two parameterized policies.<n>Our approach yields up to 2-3x improvement in sample efficiency on average over prior methods.
  arXiv  Detail & Related papers  (2025-07-10T17:57:46Z)
• Behavior Preference Regression for Offline Reinforcement Learning [0.0]
  offline reinforcement learning (RL) methods aim to learn optimal policies with access only to trajectories in a fixed dataset.<n>Policy constraint methods formulate policy learning as an optimization problem that balances maximizing reward with minimizing deviation from the policy.<n>We adapt this approach of paired comparison to Behavior Regression Preference for offline RL.<n>We empirically evaluate BPR on the widely used D4RL Locomotion and Antmaze datasets, as well as the more challenging V-D4RL suite.
  arXiv  Detail & Related papers  (2025-03-02T15:13:02Z)
• Divergence-Augmented Policy Optimization [12.980566919112034]
  This paper introduces a method to stabilize policy optimization when off-policy data are reused.<n>The idea is to include a Bregman divergence between the behavior policy that generates the data and the current policy to ensure small and safe policy updates with off-policy data.<n> Empirical experiments on Atari games show that in the data-scarce scenario where the reuse of off-policy data becomes necessary, our method can achieve better performance than other state-of-the-art deep reinforcement learning algorithms.
  arXiv  Detail & Related papers  (2025-01-25T02:35:46Z)
• Iteratively Refined Behavior Regularization for Offline Reinforcement Learning [57.10922880400715]
  In this paper, we propose a new algorithm that substantially enhances behavior-regularization based on conservative policy iteration. By iteratively refining the reference policy used for behavior regularization, conservative policy update guarantees gradually improvement. Experimental results on the D4RL benchmark indicate that our method outperforms previous state-of-the-art baselines in most tasks.
  arXiv  Detail & Related papers  (2023-06-09T07:46:24Z)
• Offline Reinforcement Learning with Closed-Form Policy Improvement Operators [88.54210578912554]
  Behavior constrained policy optimization has been demonstrated to be a successful paradigm for tackling Offline Reinforcement Learning. In this paper, we propose our closed-form policy improvement operators. We empirically demonstrate their effectiveness over state-of-the-art algorithms on the standard D4RL benchmark.
  arXiv  Detail & Related papers  (2022-11-29T06:29:26Z)
• COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation [73.17078343706909]
  offline constrained reinforcement learning (RL) problem, in which the agent aims to compute a policy that maximizes expected return while satisfying given cost constraints, learning only from a pre-collected dataset. We present an offline constrained RL algorithm that optimize the policy in the space of the stationary distribution. Our algorithm, COptiDICE, directly estimates the stationary distribution corrections of the optimal policy with respect to returns, while constraining the cost upper bound, with the goal of yielding a cost-conservative policy for actual constraint satisfaction.
  arXiv  Detail & Related papers  (2022-04-19T15:55:47Z)
• False Correlation Reduction for Offline Reinforcement Learning [115.11954432080749]
  We propose falSe COrrelation REduction (SCORE) for offline RL, a practically effective and theoretically provable algorithm. We empirically show that SCORE achieves the SoTA performance with 3.1x acceleration on various tasks in a standard benchmark (D4RL)
  arXiv  Detail & Related papers  (2021-10-24T15:34:03Z)
• OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation [59.469401906712555]
  We present an offline reinforcement learning algorithm that prevents overestimation in a more principled way. Our algorithm, OptiDICE, directly estimates the stationary distribution corrections of the optimal policy. We show that OptiDICE performs competitively with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-06-21T00:43:30Z)
• Is Pessimism Provably Efficient for Offline RL? [104.00628430454479]
  We study offline reinforcement learning (RL), which aims to learn an optimal policy based on a dataset collected a priori. We propose a pessimistic variant of the value iteration algorithm (PEVI), which incorporates an uncertainty quantifier as the penalty function.
  arXiv  Detail & Related papers  (2020-12-30T09:06:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.