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• W2295618152 abstract "Sampling and computation budgets are two of the key elements that determine the performance of a reinforcement learning algorithm. In essence, any reinforcement learning agent must sample the environment and perform some computation over the samples to decide its best action. Although very fundamental, the trade-off between sampling and computation is still not well understood. In this paper, we explore this trade-off in an actor-critic perspective. First, we propose a new RL algorithm, Dyna-MLAC, which uses model-based actor-critic updates (MLAC) within the Dyna framework. Then, we numerically indicate that the convergence time of Dyna-MLAC is smaller than pre-existing solutions, and that Dyna-MLAC allows to efficiently trade number of samples and computation time." @default.
• W2295618152 created "2016-06-24" @default.
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• W2295618152 date "2015-11-01" @default.
• W2295618152 modified "2023-10-12" @default.
• W2295618152 title "Dyna-MLAC: Trading Computational and Sample Complexities in Actor-Critic Reinforcement Learning" @default.
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• W2295618152 doi "https://doi.org/10.1109/bracis.2015.62" @default.
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