Preference-Based Multi-Agent Reinforcement Learning: Data Coverage and Algorithmic Techniques

• URL: http://arxiv.org/abs/2409.00717v3
• Date: Thu, 09 Jan 2025 11:24:44 GMT
• Title: Preference-Based Multi-Agent Reinforcement Learning: Data Coverage and Algorithmic Techniques
• Authors: Natalia Zhang, Xinqi Wang, Qiwen Cui, Runlong Zhou, Sham M. Kakade, Simon S. Du,
• Abstract summary: We study Preference-Based Multi-Agent Reinforcement Learning (PbMARL)<n>We identify the Nash equilibrium from a preference-only offline dataset in general-sum games.<n>Our findings underscore the multifaceted approach required for PbMARL.
• Score: 65.55451717632317
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We initiate the study of Preference-Based Multi-Agent Reinforcement Learning (PbMARL), exploring both theoretical foundations and empirical validations. We define the task as identifying the Nash equilibrium from a preference-only offline dataset in general-sum games, a problem marked by the challenge of sparse feedback signals. Our theory establishes the upper complexity bounds for Nash Equilibrium in effective PbMARL, demonstrating that single-policy coverage is inadequate and highlighting the importance of unilateral dataset coverage. These theoretical insights are verified through comprehensive experiments. To enhance the practical performance, we further introduce two algorithmic techniques. (1) We propose a Mean Squared Error (MSE) regularization along the time axis to achieve a more uniform reward distribution and improve reward learning outcomes. (2) We propose an additional penalty based on the distribution of the dataset to incorporate pessimism, improving stability and effectiveness during training. Our findings underscore the multifaceted approach required for PbMARL, paving the way for effective preference-based multi-agent systems.

Related papers

• Adversarial Policy Optimization for Offline Preference-based Reinforcement Learning [8.087699764574788]
  We propose an efficient algorithm for offline preference-based reinforcement learning (PbRL) APPO guarantees sample complexity bounds without relying on explicit confidence sets. To our knowledge, APPO is the first offline PbRL algorithm to offer both statistical efficiency and practical applicability.
  arXiv  Detail & Related papers  (2025-03-07T10:35:01Z)
• Distributionally Robust Reinforcement Learning with Human Feedback [13.509499718691016]
  We introduce a distributionally robust RLHF for fine-tuning large language models. Our goal is to ensure that a fine-tuned model retains its performance even when the distribution of prompts significantly differs. We show that our robust training improves the accuracy of the learned reward models on average, and markedly on some tasks, such as reasoning.
  arXiv  Detail & Related papers  (2025-03-01T15:43:39Z)
• Enhancing Fairness through Reweighting: A Path to Attain the Sufficiency Rule [23.335423207588466]
  We introduce an innovative approach to enhancing the empirical risk minimization process in model training. This scheme aims to uphold the sufficiency rule in fairness by ensuring that optimal predictors maintain consistency across diverse sub-groups.
  arXiv  Detail & Related papers  (2024-08-26T09:19:58Z)
• Joint Demonstration and Preference Learning Improves Policy Alignment with Human Feedback [58.049113055986375]
  We develop a single stage approach named Alignment with Integrated Human Feedback (AIHF) to train reward models and the policy. The proposed approach admits a suite of efficient algorithms, which can easily reduce to, and leverage, popular alignment algorithms. We demonstrate the efficiency of the proposed solutions with extensive experiments involving alignment problems in LLMs and robotic control problems in MuJoCo.
  arXiv  Detail & Related papers  (2024-06-11T01:20:53Z)
• Finite-Time Convergence and Sample Complexity of Actor-Critic Multi-Objective Reinforcement Learning [20.491176017183044]
  This paper tackles the multi-objective reinforcement learning (MORL) problem. It introduces an innovative actor-critic algorithm named MOAC which finds a policy by iteratively making trade-offs among conflicting reward signals.
  arXiv  Detail & Related papers  (2024-05-05T23:52:57Z)
• Provably Efficient Information-Directed Sampling Algorithms for Multi-Agent Reinforcement Learning [50.92957910121088]
  This work designs and analyzes a novel set of algorithms for multi-agent reinforcement learning (MARL) based on the principle of information-directed sampling (IDS) For episodic two-player zero-sum MGs, we present three sample-efficient algorithms for learning Nash equilibrium. We extend Reg-MAIDS to multi-player general-sum MGs and prove that it can learn either the Nash equilibrium or coarse correlated equilibrium in a sample efficient manner.
  arXiv  Detail & Related papers  (2024-04-30T06:48:56Z)
• 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)
• Doubly Robust Instance-Reweighted Adversarial Training [107.40683655362285]
  We propose a novel doubly-robust instance reweighted adversarial framework. Our importance weights are obtained by optimizing the KL-divergence regularized loss function. Our proposed approach outperforms related state-of-the-art baseline methods in terms of average robust performance.
  arXiv  Detail & Related papers  (2023-08-01T06:16:18Z)
• 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)
• Federated Compositional Deep AUC Maximization [58.25078060952361]
  We develop a novel federated learning method for imbalanced data by directly optimizing the area under curve (AUC) score. To the best of our knowledge, this is the first work to achieve such favorable theoretical results.
  arXiv  Detail & Related papers  (2023-04-20T05:49:41Z)
• MaxMatch: Semi-Supervised Learning with Worst-Case Consistency [149.03760479533855]
  We propose a worst-case consistency regularization technique for semi-supervised learning (SSL) We present a generalization bound for SSL consisting of the empirical loss terms observed on labeled and unlabeled training data separately. Motivated by this bound, we derive an SSL objective that minimizes the largest inconsistency between an original unlabeled sample and its multiple augmented variants.
  arXiv  Detail & Related papers  (2022-09-26T12:04:49Z)
• Offline Reinforcement Learning with Instrumental Variables in Confounded Markov Decision Processes [93.61202366677526]
  We study the offline reinforcement learning (RL) in the face of unmeasured confounders. We propose various policy learning methods with the finite-sample suboptimality guarantee of finding the optimal in-class policy.
  arXiv  Detail & Related papers  (2022-09-18T22:03:55Z)
• The Efficacy of Pessimism in Asynchronous Q-Learning [17.193902915070506]
  We develop an algorithmic framework that incorporates the principle of pessimism into asynchronous Q-learning. This framework leads to, among other things, improved sample efficiency and enhanced adaptivity in the presence of near-expert data. Our results deliver the first theoretical support for the use of pessimism principle in the presence of Markovian non-i.i.d. data.
  arXiv  Detail & Related papers  (2022-03-14T17:59:01Z)
• Revisiting Deep Semi-supervised Learning: An Empirical Distribution Alignment Framework and Its Generalization Bound [97.93945601881407]
  We propose a new deep semi-supervised learning framework called Semi-supervised Learning by Empirical Distribution Alignment (SLEDA) We show the generalization error of semi-supervised learning can be effectively bounded by minimizing the training error on labeled data. Building upon our new framework and the theoretical bound, we develop a simple and effective deep semi-supervised learning method called Augmented Distribution Alignment Network (ADA-Net)
  arXiv  Detail & Related papers  (2022-03-13T11:59:52Z)
• Deterministic and Discriminative Imitation (D2-Imitation): Revisiting Adversarial Imitation for Sample Efficiency [61.03922379081648]
  We propose an off-policy sample efficient approach that requires no adversarial training or min-max optimization. Our empirical results show that D2-Imitation is effective in achieving good sample efficiency, outperforming several off-policy extension approaches of adversarial imitation.
  arXiv  Detail & Related papers  (2021-12-11T19:36:19Z)
• Off-Policy Imitation Learning from Observations [78.30794935265425]
  Learning from Observations (LfO) is a practical reinforcement learning scenario from which many applications can benefit. We propose a sample-efficient LfO approach that enables off-policy optimization in a principled manner. Our approach is comparable with state-of-the-art locomotion in terms of both sample-efficiency and performance.
  arXiv  Detail & Related papers  (2021-02-25T21:33:47Z)

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.