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- W3100323192 abstract "We propose a reinforcement learning solution to the emph{soccer dribbling task}, a scenario in which a soccer agent has to go from the beginning to the end of a region keeping possession of the ball, as an adversary attempts to gain possession. While the adversary uses a stationary policy, the dribbler learns the best action to take at each decision point. After defining meaningful variables to represent the state space, and high-level macro-actions to incorporate domain knowledge, we describe our application of the reinforcement learning algorithm emph{Sarsa} with CMAC for function approximation. Our experiments show that, after the training period, the dribbler is able to accomplish its task against a strong adversary around 58% of the time." @default.
- W3100323192 created "2020-11-23" @default.
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- W3100323192 date "2011-08-01" @default.
- W3100323192 modified "2023-09-26" @default.
- W3100323192 title "Reinforcement learning for the soccer dribbling task" @default.
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- W3100323192 doi "https://doi.org/10.1109/cig.2011.6031994" @default.
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