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Found 2 papers, 1 papers with code
Auxiliary Reward Generation with Transition Distance Representation Learning
To achieve automatic auxiliary reward generation, we propose a novel representation learning approach that can measure the ``transition distance'' between states.
Classifying Ambiguous Identities in Hidden-Role Stochastic Games with Multi-Agent Reinforcement Learning
In this work, we develop a novel identity detection reinforcement learning (IDRL) framework that allows an agent to dynamically infer the identities of nearby agents and select an appropriate policy to accomplish the task.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
