probability-updating-games-marl
Multi-agent reinforcement learning (MARL) for relaxed probability updating games. This repository corresponds to the master thesis project "Investigating relaxed probability updating games" by Collin Aldaibis
Welcome to the repository corresponding to the master thesis project "Investigating relaxed probability updating games" by Collin Aldaibis. This repository is built and utilised to investigate Nash equilibria for general-sum probability updating games. It implements all of the mechanics in such games and provides a wrapper to port them to OpenAI Gym-like environments. Using Ray Tune and Ray RLlib, it applies the state-of-the-art Proximal Policy Optimisation (PPO) and other policy gradient learning methods to learn optimal strategies for both the host and the contestant.
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