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- W4313442862 abstract "Although most reinforcement learning research has centered on competitive games, little work has been done on applying it to co-operative multiplayer games or text-based games. Codenames is a board game that involves both asymmetric co-operation and natural language processing, which makes it an excellent candidate for advancing RL research. To my knowledge, this work is the first to formulate Codenames as a Markov Decision Process and apply some well-known reinforcement learning algorithms such as SAC, PPO, and A2C to the environment. Although none of the above algorithms converge for the Codenames environment, neither do they converge for a simplified environment called ClickPixel, except when the board size is small." @default.
- W4313442862 created "2023-01-06" @default.
- W4313442862 creator A5018777564 @default.
- W4313442862 date "2022-12-28" @default.
- W4313442862 modified "2023-10-18" @default.
- W4313442862 title "Towards automating Codenames spymasters with deep reinforcement learning" @default.
- W4313442862 doi "https://doi.org/10.48550/arxiv.2212.14104" @default.
- W4313442862 hasPublicationYear "2022" @default.
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