arXiv AI
Integrating AIRL-Based Reward Estimation into a Control Method Using Fuzzy Inference and Policy Gradient
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Trust Region Inverse Reinforcement Learning: Explicit Dual Ascent using Local Policy Updates
arXiv:2605. 11020v2 Announce Type: replace-cross Abstract: Inverse reinforcement learning (IRL) is typically formulated as maximizing entropy subject to matching the distribution of expert trajectories.
arXiv AI
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Robust Driving Control for Autonomous Vehicles: An Intelligent General-sum Constrained Adversarial Reinforcement Learning Approach
arXiv:2510. 09041v3 Announce Type: replace-cross Abstract: Deep reinforcement learning (DRL) has demonstrated remarkable success in developing autonomous driving policies.
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Reinforcement Learning via Advantage Guidance
arXiv:2610.19783v1 Announce Type: new Abstract: Reinforcement learning (RL) holds the promise of learning a policy that can outperform what is shown in the data. However, in practice, such systems su...
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Comprehensive Comparisons of Reinforcement Learning Algorithms in Autonomous Driving
arXiv:2610.19429v1 Announce Type: new Abstract: This paper presents a comprehensive comparison of four representative reinforcement learning (RL) methods in autonomous vehicle (AV) decision-making, i...
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Jul 3
Playing 20 Question Game with Policy-Based Reinforcement Learning
arXiv:1808. 07645v5 Announce Type: replace-cross Abstract: The 20 Questions (Q20) game is a well known game which encourages deductive reasoning and creativity.
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Hierarchical Reinforcement Learning with Optimal Level Synchronization Based on Flow-Based Deep Generative Model
arXiv:2107. 08183v2 Announce Type: replace Abstract: High-dimensional state and action spaces combined with sparse reward structures in reinforcement learning (RL) environments typically require advanced control architectures.