CS229 Final Report Reinforcement Learning to Play Mario
نویسندگان
چکیده
In this paper, we study applying Reinforcement Learning to design a automatic agent to play the game Super Mario Bros. One of the challenge is how to handle the complex game environment. By abstracting the game environment into a state vector and using Q learning — an algorithm oblivious to transitional probabilities — we achieve tractable computation time and fast convergence. After training for 5000 iterations, our agent is able to win about 90% of the time. We also compare and analyze the choice of different learning rate α and discount factor γ.
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