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- W2218225618 abstract "This paper explores the performance of fitted neural Q iteration for reinforcement learning in several partially observable environments, using three recurrent neural network architectures: Long Short-Term Memory, Gated Recurrent Unit and MUT1, a recurrent neural architecture evolved from a pool of several thousands candidate architectures. A variant of fitted Q iteration, based on Advantage values instead of Q values, is also explored. The results show that GRU performs significantly better than LSTM and MUT1 for most of the problems considered, requiring less training episodes and less CPU time before learning a very good policy. Advantage learning also tends to produce better results." @default.
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- W2218225618 date "2015-12-17" @default.
- W2218225618 modified "2023-09-27" @default.
- W2218225618 title "An Empirical Comparison of Neural Architectures for Reinforcement Learning in Partially Observable Environments." @default.
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