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Counterfactual-Based Action Evaluation Algorithm in Multi-Agent Reinforcement Learning
2022
Applied Sciences
Multi-agent reinforcement learning (MARL) algorithms have made great achievements in various scenarios, but there are still many problems in solving sequential social dilemmas (SSDs). In SSDs, the agent's actions not only change the instantaneous state of the environment but also affect the latent state which will, in turn, affect all agents. However, most of the current reinforcement learning algorithms focus on analyzing the value of instantaneous environment state while ignoring the study of
doi:10.3390/app12073439
fatcat:qd25rdbukbbhfkaubtyalciumi