Data-Driven Inverse Reinforcement Learning for Expert-Learner Zero-Sum Games

• URL: http://arxiv.org/abs/2301.01997v1
• Date: Thu, 5 Jan 2023 10:35:08 GMT
• Title: Data-Driven Inverse Reinforcement Learning for Expert-Learner Zero-Sum Games
• Authors: Wenqian Xue and Bosen Lian and Jialu Fan and Tianyou Chai and Frank L. Lewis
• Abstract summary: We formulate inverse reinforcement learning as an expert-learner interaction. The optimal performance intent of an expert or target agent is unknown to a learner agent. We develop an off-policy IRL algorithm that does not require knowledge of the expert and learner agent dynamics.
• Score: 30.720112378448285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we formulate inverse reinforcement learning (IRL) as an expert-learner interaction whereby the optimal performance intent of an expert or target agent is unknown to a learner agent. The learner observes the states and controls of the expert and hence seeks to reconstruct the expert's cost function intent and thus mimics the expert's optimal response. Next, we add non-cooperative disturbances that seek to disrupt the learning and stability of the learner agent. This leads to the formulation of a new interaction we call zero-sum game IRL. We develop a framework to solve the zero-sum game IRL problem that is a modified extension of RL policy iteration (PI) to allow unknown expert performance intentions to be computed and non-cooperative disturbances to be rejected. The framework has two parts: a value function and control action update based on an extension of PI, and a cost function update based on standard inverse optimal control. Then, we eventually develop an off-policy IRL algorithm that does not require knowledge of the expert and learner agent dynamics and performs single-loop learning. Rigorous proofs and analyses are given. Finally, simulation experiments are presented to show the effectiveness of the new approach.

Related papers

• Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching [23.600285251963395]
  In inverse reinforcement learning (IRL), an agent seeks to replicate expert demonstrations through interactions with the environment. Traditionally, IRL is treated as an adversarial game, where an adversary searches over reward models, and a learner optimize the reward through repeated RL procedures. We propose a novel approach to IRL by direct policy optimization, exploiting a linear factorization of the return as the inner product of successor features and a reward vector.
  arXiv  Detail & Related papers  (2024-11-11T14:05:50Z)
• RILe: Reinforced Imitation Learning [60.63173816209543]
  RILe is a novel trainer-student system that learns a dynamic reward function based on the student's performance and alignment with expert demonstrations. RILe enables better performance in complex settings where traditional methods falter, outperforming existing methods by 2x in complex simulated robot-locomotion tasks.
  arXiv  Detail & Related papers  (2024-06-12T17:56:31Z)
• RLIF: Interactive Imitation Learning as Reinforcement Learning [56.997263135104504]
  We show how off-policy reinforcement learning can enable improved performance under assumptions that are similar but potentially even more practical than those of interactive imitation learning. Our proposed method uses reinforcement learning with user intervention signals themselves as rewards. This relaxes the assumption that intervening experts in interactive imitation learning should be near-optimal and enables the algorithm to learn behaviors that improve over the potential suboptimal human expert.
  arXiv  Detail & Related papers  (2023-11-21T21:05:21Z)
• CLARE: Conservative Model-Based Reward Learning for Offline Inverse Reinforcement Learning [26.05184273238923]
  This work aims to tackle a major challenge in offline Inverse Reinforcement Learning (IRL) We devise a principled algorithm (namely CLARE) that solves offline IRL efficiently via integrating "conservatism" into a learned reward function. Our theoretical analysis provides an upper bound on the return gap between the learned policy and the expert policy.
  arXiv  Detail & Related papers  (2023-02-09T17:16:29Z)
• Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels [112.63440666617494]
  Reinforcement learning algorithms can succeed but require large amounts of interactions between the agent and the environment. We propose a new method to solve it, using unsupervised model-based RL, for pre-training the agent. We show robust performance on the Real-Word RL benchmark, hinting at resiliency to environment perturbations during adaptation.
  arXiv  Detail & Related papers  (2022-09-24T14:22:29Z)
• Basis for Intentions: Efficient Inverse Reinforcement Learning using Past Experience [89.30876995059168]
  inverse reinforcement learning (IRL) -- inferring the reward function of an agent from observing its behavior. This paper addresses the problem of IRL -- inferring the reward function of an agent from observing its behavior.
  arXiv  Detail & Related papers  (2022-08-09T17:29:49Z)
• Retrieval-Augmented Reinforcement Learning [63.32076191982944]
  We train a network to map a dataset of past experiences to optimal behavior. The retrieval process is trained to retrieve information from the dataset that may be useful in the current context. We show that retrieval-augmented R2D2 learns significantly faster than the baseline R2D2 agent and achieves higher scores.
  arXiv  Detail & Related papers  (2022-02-17T02:44:05Z)
• PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training [94.87393610927812]
  We present an off-policy, interactive reinforcement learning algorithm that capitalizes on the strengths of both feedback and off-policy learning. We demonstrate that our approach is capable of learning tasks of higher complexity than previously considered by human-in-the-loop methods.
  arXiv  Detail & Related papers  (2021-06-09T14:10:50Z)
• Learning without Knowing: Unobserved Context in Continuous Transfer Reinforcement Learning [16.814772057210366]
  We consider a transfer Reinforcement Learning problem in continuous state and action spaces under unobserved contextual information. Our goal is to use the context-aware expert data to learn an optimal context-unaware policy for the learner using only a few new data samples.
  arXiv  Detail & Related papers  (2021-06-07T17:49:22Z)
• Off-Policy Adversarial Inverse Reinforcement Learning [0.0]
  Adversarial Imitation Learning (AIL) is a class of algorithms in Reinforcement learning (RL) We propose an Off-Policy Adversarial Inverse Reinforcement Learning (Off-policy-AIRL) algorithm which is sample efficient as well as gives good imitation performance.
  arXiv  Detail & Related papers  (2020-05-03T16:51:40Z)

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.