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Found 3 papers, 2 papers with code
Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey
Reinforcement Learning (RL) is a potent tool for sequential decision-making and has achieved performance surpassing human capabilities across many challenging real-world tasks.
Preferred-Action-Optimized Diffusion Policies for Offline Reinforcement Learning
Meanwhile, based on the diffusion model, preferred actions within the same behavior distribution are automatically generated through the critic function.
POCE: Primal Policy Optimization with Conservative Estimation for Multi-constraint Offline Reinforcement Learning
Multi-constraint offline reinforcement learning (RL) promises to learn policies that satisfy both cumulative and state-wise costs from offline datasets.
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