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Found 2 papers, 1 papers with code
Logic-based Reward Shaping for Multi-Agent Reinforcement Learning
We present a novel method for semi-centralized logic-based MARL reward shaping that is scalable in the number of agents and evaluate it in multiple scenarios.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Safe Multi-Agent Reinforcement Learning via Shielding
Multi-agent reinforcement learning (MARL) has been increasingly used in a wide range of safety-critical applications, which require guaranteed safety (e. g., no unsafe states are ever visited) during the learning process. Unfortunately, current MARL methods do not have safety guarantees.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
