CL-MARL uses an adaptive curriculum scheduler called FlexDiff and Counterfactual Group Relative Policy Advantage to break static-difficulty training in MARL and achieve higher win rates on hard StarCraft maps.
Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey,
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Co-training an SDC and 12 pedestrians with MAPPO in a MARL setup yields 78% goal success and 14% collisions versus 35% goals and 33% for the best rule-based baseline, with jaywalking linked to 62% of collisions despite being only 13% of events.
citing papers explorer
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Overcoming Environmental Meta-Stationarity in MARL via Adaptive Curriculum and Counterfactual Group Advantage
CL-MARL uses an adaptive curriculum scheduler called FlexDiff and Counterfactual Group Relative Policy Advantage to break static-difficulty training in MARL and achieve higher win rates on hard StarCraft maps.
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Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty
Co-training an SDC and 12 pedestrians with MAPPO in a MARL setup yields 78% goal success and 14% collisions versus 35% goals and 33% for the best rule-based baseline, with jaywalking linked to 62% of collisions despite being only 13% of events.