Convergence of a model-free entropy-regularized inverse reinforcement learning algorithm

• URL: http://arxiv.org/abs/2403.16829v2
• Date: Tue, 23 Apr 2024 13:54:27 GMT
• Title: Convergence of a model-free entropy-regularized inverse reinforcement learning algorithm
• Authors: Titouan Renard, Andreas Schlaginhaufen, Tingting Ni, Maryam Kamgarpour,
• Abstract summary: inverse reinforcement learning (IRL) aims to recover a reward for which the expert is optimal. This work proposes a model-free algorithm to solve the entropy-regularized IRL problem.
• Score: 6.481009996429766
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Given a dataset of expert demonstrations, inverse reinforcement learning (IRL) aims to recover a reward for which the expert is optimal. This work proposes a model-free algorithm to solve entropy-regularized IRL problem. In particular, we employ a stochastic gradient descent update for the reward and a stochastic soft policy iteration update for the policy. Assuming access to a generative model, we prove that our algorithm is guaranteed to recover a reward for which the expert is $\varepsilon$-optimal using $\mathcal{O}(1/\varepsilon^{2})$ samples of the Markov decision process (MDP). Furthermore, with $\mathcal{O}(1/\varepsilon^{4})$ samples we prove that the optimal policy corresponding to the recovered reward is $\varepsilon$-close to the expert policy in total variation distance.

Related papers

• Projection by Convolution: Optimal Sample Complexity for Reinforcement Learning in Continuous-Space MDPs [56.237917407785545]
  We consider the problem of learning an $varepsilon$-optimal policy in a general class of continuous-space Markov decision processes (MDPs) having smooth Bellman operators. Key to our solution is a novel projection technique based on ideas from harmonic analysis. Our result bridges the gap between two popular but conflicting perspectives on continuous-space MDPs.
  arXiv  Detail & Related papers  (2024-05-10T09:58:47Z)
• Provable Policy Gradient Methods for Average-Reward Markov Potential Games [15.136494705127564]
  We study Markov potential games under the infinite horizon average reward criterion. We prove that both algorithms based on independent policy gradient and independent natural policy gradient converge globally to a Nash equilibrium for the average reward criterion.
  arXiv  Detail & Related papers  (2024-03-09T00:20:33Z)
• Provably Robust Temporal Difference Learning for Heavy-Tailed Rewards [27.209606183563853]
  We establish that temporal difference (TD) learning with a dynamic gradient clipping mechanism can be provably robustified against heavy-tailed reward distributions. We show that a robust variant of NAC based on TD learning achieves $tildemathcalO(varepsilon-frac1p)$ sample complexity.
  arXiv  Detail & Related papers  (2023-06-20T11:12:21Z)
• Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees [56.848265937921354]
  Inverse reinforcement learning (IRL) aims to recover the reward function and the associated optimal policy. Many algorithms for IRL have an inherently nested structure. We develop a novel single-loop algorithm for IRL that does not compromise reward estimation accuracy.
  arXiv  Detail & Related papers  (2022-10-04T17:13:45Z)
• Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation [92.99933928528797]
  We study the model-based reward-free reinforcement learning with linear function approximation for episodic Markov decision processes (MDPs) In the planning phase, the agent is given a specific reward function and uses samples collected from the exploration phase to learn a good policy. We show that to obtain an $epsilon$-optimal policy for arbitrary reward function, UCRL-RFE needs to sample at most $tilde O(H4d(H + d)epsilon-2)$ episodes.
  arXiv  Detail & Related papers  (2021-10-12T23:03:58Z)
• Randomized Exploration for Reinforcement Learning with General Value Function Approximation [122.70803181751135]
  We propose a model-free reinforcement learning algorithm inspired by the popular randomized least squares value iteration (RLSVI) algorithm. Our algorithm drives exploration by simply perturbing the training data with judiciously chosen i.i.d. scalar noises. We complement the theory with an empirical evaluation across known difficult exploration tasks.
  arXiv  Detail & Related papers  (2021-06-15T02:23:07Z)
• On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method [38.34416337932712]
  Policy gives rise to a rich class of reinforcement learning (RL) methods, for example the REINFORCE. Yet the best known sample complexity result for such methods to find an $epsilon$-optimal policy is $mathcalO(epsilon-3)$, which is suboptimal. We study the fundamental convergence properties and sample efficiency of first-order policy optimization method.
  arXiv  Detail & Related papers  (2021-02-17T07:06:19Z)
• Adaptive Reward-Free Exploration [48.98199700043158]
  We show that our reward-free UCRL algorithm can be seen as a variant of an algorithm of Fiechter from 1994. We further investigate the relative complexities of reward-free exploration and best-policy identification.
  arXiv  Detail & Related papers  (2020-06-11T09:58:03Z)
• Reward-Free Exploration for Reinforcement Learning [82.3300753751066]
  We propose a new "reward-free RL" framework to isolate the challenges of exploration. We give an efficient algorithm that conducts $tildemathcalO(S2Amathrmpoly(H)/epsilon2)$ episodes of exploration. We also give a nearly-matching $Omega(S2AH2/epsilon2)$ lower bound, demonstrating the near-optimality of our algorithm in this setting.
  arXiv  Detail & Related papers  (2020-02-07T14:03:38Z)

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.