Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching

• URL: http://arxiv.org/abs/2411.07007v1
• Date: Mon, 11 Nov 2024 14:05:50 GMT
• Title: Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching
• Authors: Arnav Kumar Jain, Harley Wiltzer, Jesse Farebrother, Irina Rish, Glen Berseth, Sanjiban Choudhury,
• Abstract summary: 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.
• Score: 23.600285251963395
• License:
• Abstract: 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 optimizes the reward through repeated RL procedures. This game-solving approach is both computationally expensive and difficult to stabilize. In this work, 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, we design an IRL algorithm by policy gradient descent on the gap between the learner and expert features. Our non-adversarial method does not require learning a reward function and can be solved seamlessly with existing actor-critic RL algorithms. Remarkably, our approach works in state-only settings without expert action labels, a setting which behavior cloning (BC) cannot solve. Empirical results demonstrate that our method learns from as few as a single expert demonstration and achieves improved performance on various control tasks.

Related papers

• 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)
• Coherent Soft Imitation Learning [17.345411907902932]
  Imitation learning methods seek to learn from an expert either through behavioral cloning (BC) of the policy or inverse reinforcement learning (IRL) of the reward. This work derives an imitation method that captures the strengths of both BC and IRL.
  arXiv  Detail & Related papers  (2023-05-25T21:54:22Z)
• Data-Driven Inverse Reinforcement Learning for Expert-Learner Zero-Sum Games [30.720112378448285]
  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.
  arXiv  Detail & Related papers  (2023-01-05T10:35:08Z)
• 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)
• Reward Uncertainty for Exploration in Preference-based Reinforcement Learning [88.34958680436552]
  We present an exploration method specifically for preference-based reinforcement learning algorithms. Our main idea is to design an intrinsic reward by measuring the novelty based on learned reward. Our experiments show that exploration bonus from uncertainty in learned reward improves both feedback- and sample-efficiency of preference-based RL algorithms.
  arXiv  Detail & Related papers  (2022-05-24T23:22:10Z)
• Jump-Start Reinforcement Learning [68.82380421479675]
  We present a meta algorithm that can use offline data, demonstrations, or a pre-existing policy to initialize an RL policy. In particular, we propose Jump-Start Reinforcement Learning (JSRL), an algorithm that employs two policies to solve tasks. We show via experiments that JSRL is able to significantly outperform existing imitation and reinforcement learning algorithms.
  arXiv  Detail & Related papers  (2022-04-05T17:25:22Z)
• 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)
• Self-Imitation Advantage Learning [43.8107780378031]
  Self-imitation learning is a Reinforcement Learning method that encourages actions whose returns were higher than expected. We propose a novel generalization of self-imitation learning for off-policy RL, based on a modification of the Bellman optimality operator.
  arXiv  Detail & Related papers  (2020-12-22T13:21:50Z)
• Non-Adversarial Imitation Learning and its Connections to Adversarial Methods [21.89749623434729]
  We present a framework for non-adversarial imitation learning. The resulting algorithms are similar to their adversarial counterparts. We also show that our non-adversarial formulation can be used to derive novel algorithms.
  arXiv  Detail & Related papers  (2020-08-08T13:43:06Z)
• Forgetful Experience Replay in Hierarchical Reinforcement Learning from Demonstrations [55.41644538483948]
  In this paper, we propose a combination of approaches that allow the agent to use low-quality demonstrations in complex vision-based environments. Our proposed goal-oriented structuring of replay buffer allows the agent to automatically highlight sub-goals for solving complex hierarchical tasks in demonstrations. The solution based on our algorithm beats all the solutions for the famous MineRL competition and allows the agent to mine a diamond in the Minecraft environment.
  arXiv  Detail & Related papers  (2020-06-17T15:38:40Z)
• 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.