Internal State-Based Policy Gradient Methods for Partially Observable Markov Potential Games
Internal State-Based Policy Gradient Methods for Partially Observable Markov Potential Games explores A theoretical framework for multi-agent reinforcement learning in partially observable games using internal states and a policy gradient method, with a proven convergence bound.. Commercial viability score: 4/10 in Multi-Agent RL.
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