Beyond Non-Expert Demonstrations: Outcome-Driven Action Constraint for Offline Reinforcement Learning

• URL: http://arxiv.org/abs/2504.01719v2
• Date: Thu, 03 Apr 2025 01:40:35 GMT
• Title: Beyond Non-Expert Demonstrations: Outcome-Driven Action Constraint for Offline Reinforcement Learning
• Authors: Ke Jiang, Wen Jiang, Yao Li, Xiaoyang Tan,
• Abstract summary: We address the challenge of offline reinforcement learning using realistic data, specifically non-expert data collected through sub-optimal behavior policies.<n>Under such circumstance, the learned policy must be safe enough to manage distribution shift while maintaining sufficient flexibility to deal with bad demonstrations from offline data.<n>We introduce a novel method called Outcome-Driven Action Flexibility (ODAF), which seeks to reduce reliance on the empirical action distribution of the behavior policy, hence reducing the negative impact of those bad demonstrations.
• Score: 17.601574372211232
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We address the challenge of offline reinforcement learning using realistic data, specifically non-expert data collected through sub-optimal behavior policies. Under such circumstance, the learned policy must be safe enough to manage distribution shift while maintaining sufficient flexibility to deal with non-expert (bad) demonstrations from offline data.To tackle this issue, we introduce a novel method called Outcome-Driven Action Flexibility (ODAF), which seeks to reduce reliance on the empirical action distribution of the behavior policy, hence reducing the negative impact of those bad demonstrations.To be specific, a new conservative reward mechanism is developed to deal with distribution shift by evaluating actions according to whether their outcomes meet safety requirements - remaining within the state support area, rather than solely depending on the actions' likelihood based on offline data.Besides theoretical justification, we provide empirical evidence on widely used MuJoCo and various maze benchmarks, demonstrating that our ODAF method, implemented using uncertainty quantification techniques, effectively tolerates unseen transitions for improved "trajectory stitching," while enhancing the agent's ability to learn from realistic non-expert data.

Related papers

• Offline Action-Free Learning of Ex-BMDPs by Comparing Diverse Datasets [87.62730694973696]
  This paper introduces CRAFT, a sample-efficient algorithm leveraging differences in controllable feature dynamics across agents to learn representations.<n>We provide theoretical guarantees for CRAFT's performance and demonstrate its feasibility on a toy example.
  arXiv  Detail & Related papers  (2025-03-26T22:05:57Z)
• DiffPoGAN: Diffusion Policies with Generative Adversarial Networks for Offline Reinforcement Learning [22.323173093804897]
  offline reinforcement learning can learn optimal policies from pre-collected offline datasets without interacting with the environment. Recent works address this issue by employing generative adversarial networks (GANs) Inspired by the diffusion, we propose a new offline RL method named Diffusion Policies with Generative Adversarial Networks (DiffPoGAN)
  arXiv  Detail & Related papers  (2024-06-13T13:15:40Z)
• Inverse-RLignment: Large Language Model Alignment from Demonstrations through Inverse Reinforcement Learning [62.05713042908654]
  We introduce Alignment from Demonstrations (AfD), a novel approach leveraging high-quality demonstration data to overcome these challenges.<n>We formalize AfD within a sequential decision-making framework, highlighting its unique challenge of missing reward signals.<n> Practically, we propose a computationally efficient algorithm that extrapolates over a tailored reward model for AfD.
  arXiv  Detail & Related papers  (2024-05-24T15:13:53Z)
• Pessimistic Causal Reinforcement Learning with Mediators for Confounded Offline Data [17.991833729722288]
  We propose a novel policy learning algorithm, PESsimistic CAusal Learning (PESCAL) Our key observation is that, by incorporating auxiliary variables that mediate the effect of actions on system dynamics, it is sufficient to learn a lower bound of the mediator distribution function, instead of the Q-function. We provide theoretical guarantees for the algorithms we propose, and demonstrate their efficacy through simulations, as well as real-world experiments utilizing offline datasets from a leading ride-hailing platform.
  arXiv  Detail & Related papers  (2024-03-18T14:51:19Z)
• Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation [13.325600043256552]
  We propose a semi-offline evaluation framework, where human users provide annotations of unobserved counterfactual trajectories. Our framework, combined with principled human-centered design of annotation solicitation, can enable the application of reinforcement learning in high-stakes domains.
  arXiv  Detail & Related papers  (2023-10-26T04:41:19Z)
• Good Better Best: Self-Motivated Imitation Learning for noisy Demonstrations [12.627982138086892]
  Imitation Learning aims to discover a policy by minimizing the discrepancy between the agent's behavior and expert demonstrations. In this paper, we introduce Self-Motivated Imitation LEarning (SMILE), a method capable of progressively filtering out demonstrations collected by policies deemed inferior to the current policy.
  arXiv  Detail & Related papers  (2023-10-24T13:09:56Z)
• Mimicking Better by Matching the Approximate Action Distribution [48.95048003354255]
  We introduce MAAD, a novel, sample-efficient on-policy algorithm for Imitation Learning from Observations. We show that it requires considerable fewer interactions to achieve expert performance, outperforming current state-of-the-art on-policy methods.
  arXiv  Detail & Related papers  (2023-06-16T12:43:47Z)
• When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning [62.00672284480755]
  This paper aims to recover the structure of rewards and environment dynamics that underlie observed actions in a fixed, finite set of demonstrations from an expert agent. Accurate models of expertise in executing a task has applications in safety-sensitive applications such as clinical decision making and autonomous driving.
  arXiv  Detail & Related papers  (2023-02-15T04:14:20Z)
• Discriminator-Guided Model-Based Offline Imitation Learning [11.856949845359853]
  offline imitation learning (IL) is a powerful method to solve decision-making problems from expert demonstrations without reward labels. We propose the Discriminator-guided Model-based offline Learning (DMIL) framework, which introduces a discriminator to simultaneously distinguish the dynamics correctness and suboptimality of model rollout data. Experimental results show that DMIL and its extension achieve superior performance and robustness compared to state-of-the-art offline IL methods under small datasets.
  arXiv  Detail & Related papers  (2022-07-01T07:28:18Z)
• A Regularized Implicit Policy for Offline Reinforcement Learning [54.7427227775581]
  offline reinforcement learning enables learning from a fixed dataset, without further interactions with the environment. We propose a framework that supports learning a flexible yet well-regularized fully-implicit policy. Experiments and ablation study on the D4RL dataset validate our framework and the effectiveness of our algorithmic designs.
  arXiv  Detail & Related papers  (2022-02-19T20:22:04Z)
• Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning [63.53407136812255]
  Offline Reinforcement Learning promises to learn effective policies from previously-collected, static datasets without the need for exploration. Existing Q-learning and actor-critic based off-policy RL algorithms fail when bootstrapping from out-of-distribution (OOD) actions or states. We propose Uncertainty Weighted Actor-Critic (UWAC), an algorithm that detects OOD state-action pairs and down-weights their contribution in the training objectives accordingly.
  arXiv  Detail & Related papers  (2021-05-17T20:16:46Z)
• Strictly Batch Imitation Learning by Energy-based Distribution Matching [104.33286163090179]
  Consider learning a policy purely on the basis of demonstrated behavior -- that is, with no access to reinforcement signals, no knowledge of transition dynamics, and no further interaction with the environment. One solution is simply to retrofit existing algorithms for apprenticeship learning to work in the offline setting. But such an approach leans heavily on off-policy evaluation or offline model estimation, and can be indirect and inefficient. We argue that a good solution should be able to explicitly parameterize a policy, implicitly learn from rollout dynamics, and operate in an entirely offline fashion.
  arXiv  Detail & Related papers  (2020-06-25T03:27:59Z)

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.