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News/Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty
arxiv
PublishedMay 22, 2026 at 4:00 AM
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Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty

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Publisher summary· verbatim

arXiv:2605.20255v1 Announce Type: cross Abstract: Simulation-based testing of self-driving cars (SDCs) typically relies on scripted or simplified pedestrian models that do not capture the heterogeneity and uncertainty of real human crossing behavior. This limits the realism of safety assessments, es

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    Multi-Agent Proximal Policy Optimization (MAPPO)
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Mentioned models
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  • 01
    Multi-Agent Proximal Policy Optimization (MAPPO)
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Read original ↗All from arxiv →
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arxivMAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation58marxivCounterfactual Model Testing Under Distribution Shift58marxivCentralized Critics for N-Agent Ad Hoc Teamwork58marxivWhere and What to Reward? Understanding Generalization Between Verifiable and Open-Ended Domains in Reinforcement Learning58m
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