Provably Efficient Exploration in Inverse Constrained Reinforcement Learning

• URL: http://arxiv.org/abs/2409.15963v3
• Date: Tue, 1 Oct 2024 02:00:50 GMT
• Title: Provably Efficient Exploration in Inverse Constrained Reinforcement Learning
• Authors: Bo Yue, Jian Li, Guiliang Liu,
• Abstract summary: Inverse Constrained Reinforcement Learning seeks to recover constraints from expert demonstrations in a data-driven manner. We introduce a strategic exploration framework with guaranteed efficiency. Motivated by our findings, we propose two exploratory algorithms to achieve efficient constraint inference.
• Score: 12.178081346315523
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To obtain the optimal constraints in complex environments, Inverse Constrained Reinforcement Learning (ICRL) seeks to recover these constraints from expert demonstrations in a data-driven manner. Existing ICRL algorithms collect training samples from an interactive environment. However, the efficacy and efficiency of these sampling strategies remain unknown. To bridge this gap, we introduce a strategic exploration framework with guaranteed efficiency. Specifically, we define a feasible constraint set for ICRL problems and investigate how expert policy and environmental dynamics influence the optimality of constraints. Motivated by our findings, we propose two exploratory algorithms to achieve efficient constraint inference via 1) dynamically reducing the bounded aggregate error of cost estimation and 2) strategically constraining the exploration policy. Both algorithms are theoretically grounded with tractable sample complexity. We empirically demonstrate the performance of our algorithms under various environments.

Related papers

• Evolutionary Algorithm with Detection Region Method for Constrained Multi-Objective Problems with Binary Constraints [9.764702512419946]
  This paper proposes a novel algorithm called DRMCMO based on the detection region method. In DRMCMO, detection regions dynamic monitor feasible solutions to enhance convergence, helping the population escape local optima. We have modified three existing test suites to serve as benchmark test problems for CMOPs with binary constraints.
  arXiv  Detail & Related papers  (2024-11-13T08:39:04Z)
• EVOLvE: Evaluating and Optimizing LLMs For Exploration [76.66831821738927]
  Large language models (LLMs) remain under-studied in scenarios requiring optimal decision-making under uncertainty. We measure LLMs' (in)ability to make optimal decisions in bandits, a state-less reinforcement learning setting relevant to many applications. Motivated by the existence of optimal exploration algorithms, we propose efficient ways to integrate this algorithmic knowledge into LLMs.
  arXiv  Detail & Related papers  (2024-10-08T17:54:03Z)
• Resilient Constrained Reinforcement Learning [87.4374430686956]
  We study a class of constrained reinforcement learning (RL) problems in which multiple constraint specifications are not identified before study. It is challenging to identify appropriate constraint specifications due to the undefined trade-off between the reward training objective and the constraint satisfaction. We propose a new constrained RL approach that searches for policy and constraint specifications together.
  arXiv  Detail & Related papers  (2023-12-28T18:28:23Z)
• Adaptive trajectory-constrained exploration strategy for deep reinforcement learning [6.589742080994319]
  Deep reinforcement learning (DRL) faces significant challenges in addressing the hard-exploration problems in tasks with sparse or deceptive rewards and large state spaces. We propose an efficient adaptive trajectory-constrained exploration strategy for DRL. We conduct experiments on two large 2D grid world mazes and several MuJoCo tasks.
  arXiv  Detail & Related papers  (2023-12-27T07:57:15Z)
• Controlling Continuous Relaxation for Combinatorial Optimization [0.0]
  Unsupervised learning solvers for optimization (CO) train a neural network that generates a soft solution using a continuous relaxation strategy. This study introduces a Continuous Relaxation Anneal (CRA) strategy, an effective-free learning method for UL-based solvers.
  arXiv  Detail & Related papers  (2023-09-29T04:23:58Z)
• Evolving Constrained Reinforcement Learning Policy [5.4444944707433525]
  We propose a novel evolutionary constrained reinforcement learning algorithm, which adaptively balances the reward and constraint violation with ranking. Experiments on robotic control benchmarks show that our ECRL achieves outstanding performance compared to state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2023-04-19T03:54:31Z)
• Reinforcement Learning with Stepwise Fairness Constraints [50.538878453547966]
  We introduce the study of reinforcement learning with stepwise fairness constraints. We provide learning algorithms with strong theoretical guarantees in regard to policy optimality and fairness violation.
  arXiv  Detail & Related papers  (2022-11-08T04:06:23Z)
• Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks [59.419152768018506]
  We show that any optimal policy necessarily satisfies the k-SP constraint. We propose a novel cost function that penalizes the policy violating SP constraint, instead of completely excluding it. Our experiments on MiniGrid, DeepMind Lab, Atari, and Fetch show that the proposed method significantly improves proximal policy optimization (PPO)
  arXiv  Detail & Related papers  (2021-07-13T21:39:21Z)
• DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation [57.358212277226315]
  In imitation learning from observation IfO, a learning agent seeks to imitate a demonstrating agent using only observations of the demonstrated behavior without access to the control signals generated by the demonstrator. Recent methods based on adversarial imitation learning have led to state-of-the-art performance on IfO problems, but they typically suffer from high sample complexity due to a reliance on data-inefficient, model-free reinforcement learning algorithms. This issue makes them impractical to deploy in real-world settings, where gathering samples can incur high costs in terms of time, energy, and risk. We propose a more data-efficient IfO algorithm
  arXiv  Detail & Related papers  (2021-03-31T23:46:32Z)
• Discrete Action On-Policy Learning with Action-Value Critic [72.20609919995086]
  Reinforcement learning (RL) in discrete action space is ubiquitous in real-world applications, but its complexity grows exponentially with the action-space dimension. We construct a critic to estimate action-value functions, apply it on correlated actions, and combine these critic estimated action values to control the variance of gradient estimation. These efforts result in a new discrete action on-policy RL algorithm that empirically outperforms related on-policy algorithms relying on variance control techniques.
  arXiv  Detail & Related papers  (2020-02-10T04:23:09Z)

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.