Hindsight Preference Learning for Offline Preference-based Reinforcement Learning

• URL: http://arxiv.org/abs/2407.04451v1
• Date: Fri, 5 Jul 2024 12:05:37 GMT
• Title: Hindsight Preference Learning for Offline Preference-based Reinforcement Learning
• Authors: Chen-Xiao Gao, Shengjun Fang, Chenjun Xiao, Yang Yu, Zongzhang Zhang,
• Abstract summary: Offline preference-based reinforcement learning (RL) focuses on optimizing policies using human preferences between pairs of trajectory segments selected from an offline dataset. We propose to model human preferences using rewards conditioned on future outcomes of the trajectory segments. Our proposed method, Hindsight Preference Learning (HPL), can facilitate credit assignment by taking full advantage of vast trajectory data available in massive unlabeled datasets.
• Score: 22.870967604847458
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Offline preference-based reinforcement learning (RL), which focuses on optimizing policies using human preferences between pairs of trajectory segments selected from an offline dataset, has emerged as a practical avenue for RL applications. Existing works rely on extracting step-wise reward signals from trajectory-wise preference annotations, assuming that preferences correlate with the cumulative Markovian rewards. However, such methods fail to capture the holistic perspective of data annotation: Humans often assess the desirability of a sequence of actions by considering the overall outcome rather than the immediate rewards. To address this challenge, we propose to model human preferences using rewards conditioned on future outcomes of the trajectory segments, i.e. the hindsight information. For downstream RL optimization, the reward of each step is calculated by marginalizing over possible future outcomes, the distribution of which is approximated by a variational auto-encoder trained using the offline dataset. Our proposed method, Hindsight Preference Learning (HPL), can facilitate credit assignment by taking full advantage of vast trajectory data available in massive unlabeled datasets. Comprehensive empirical studies demonstrate the benefits of HPL in delivering robust and advantageous rewards across various domains. Our code is publicly released at https://github.com/typoverflow/WiseRL.

Related papers

• Hybrid Preferences: Learning to Route Instances for Human vs. AI Feedback [87.37721254914476]
  We introduce a routing framework that combines inputs from humans and LMs to achieve better annotation quality. We train a performance prediction model to predict a reward model's performance on an arbitrary combination of human and LM annotations. We show that the selected hybrid mixture achieves better reward model performance compared to using either one exclusively.
  arXiv  Detail & Related papers  (2024-10-24T20:04:15Z)
• Reward-Augmented Data Enhances Direct Preference Alignment of LLMs [56.24431208419858]
  We introduce reward-conditioned Large Language Models (LLMs) that learn from the entire spectrum of response quality within the dataset. We propose an effective yet simple data relabeling method that conditions the preference pairs on quality scores to construct a reward-augmented dataset.
  arXiv  Detail & Related papers  (2024-10-10T16:01:51Z)
• Preference-Guided Reinforcement Learning for Efficient Exploration [7.83845308102632]
  We introduce LOPE: Learning Online with trajectory Preference guidancE, an end-to-end preference-guided RL framework. Our intuition is that LOPE directly adjusts the focus of online exploration by considering human feedback as guidance. LOPE outperforms several state-of-the-art methods regarding convergence rate and overall performance.
  arXiv  Detail & Related papers  (2024-07-09T02:11:12Z)
• Preference Elicitation for Offline Reinforcement Learning [59.136381500967744]
  We propose Sim-OPRL, an offline preference-based reinforcement learning algorithm. Our algorithm employs a pessimistic approach for out-of-distribution data, and an optimistic approach for acquiring informative preferences about the optimal policy.
  arXiv  Detail & Related papers  (2024-06-26T15:59:13Z)
• Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data [102.16105233826917]
  Learning from preference labels plays a crucial role in fine-tuning large language models. There are several distinct approaches for preference fine-tuning, including supervised learning, on-policy reinforcement learning (RL), and contrastive learning.
  arXiv  Detail & Related papers  (2024-04-22T17:20:18Z)
• Learning Goal-Conditioned Policies from Sub-Optimal Offline Data via Metric Learning [22.174803826742963]
  We address the problem of learning optimal behavior from sub-optimal datasets for goal-conditioned offline reinforcement learning. We propose the use of metric learning to approximate the optimal value function for goal-conditioned offline RL problems. We show that our method estimates optimal behaviors from severely sub-optimal offline datasets without suffering from out-of-distribution estimation errors.
  arXiv  Detail & Related papers  (2024-02-16T16:46:53Z)
• Sample Complexity of Preference-Based Nonparametric Off-Policy Evaluation with Deep Networks [58.469818546042696]
  We study the sample efficiency of OPE with human preference and establish a statistical guarantee for it. By appropriately selecting the size of a ReLU network, we show that one can leverage any low-dimensional manifold structure in the Markov decision process.
  arXiv  Detail & Related papers  (2023-10-16T16:27:06Z)
• Benchmarks and Algorithms for Offline Preference-Based Reward Learning [41.676208473752425]
  We propose an approach that uses an offline dataset to craft preference queries via pool-based active learning. Our proposed approach does not require actual physical rollouts or an accurate simulator for either the reward learning or policy optimization steps.
  arXiv  Detail & Related papers  (2023-01-03T23:52:16Z)
• Data-Driven Offline Decision-Making via Invariant Representation Learning [97.49309949598505]
  offline data-driven decision-making involves synthesizing optimized decisions with no active interaction. A key challenge is distributional shift: when we optimize with respect to the input into a model trained from offline data, it is easy to produce an out-of-distribution (OOD) input that appears erroneously good. In this paper, we formulate offline data-driven decision-making as domain adaptation, where the goal is to make accurate predictions for the value of optimized decisions.
  arXiv  Detail & Related papers  (2022-11-21T11:01:37Z)

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.