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>Parallel reinforcement learning for weighted multi-criteria model with adaptive margin
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Parallel reinforcement learning for weighted multi-criteria model with adaptive margin
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机译:自适应余量加权多准则模型的并行强化学习
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摘要
Reinforcement learning (RL) for a linear family of tasks is described in this paper. The key of our discussion is nonlinearity of the optimal solution even if the task family is linear; we cannot obtain the optimal policy using a naive approach. Although an algorithm exists for calculating the equivalent result to Q-learning for each task simultaneously, it presents the problem of explosion of set sizes. We therefore introduce adaptive margins to overcome this difficulty.
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