Self-Paced Prioritized Curriculum Learning With Coverage Penalty in Deep Reinforcement Learning.
IEEE Transactions on Neural Networks and Learning Systems(2018)
摘要
In this paper, a new training paradigm is proposed for deep reinforcement learning using self-paced prioritized curriculum learning with coverage penalty. The proposed deep curriculum reinforcement learning (DCRL) takes the most advantage of experience replay by adaptively selecting appropriate transitions from replay memory based on the complexity of each transition. The criteria of complexity in...
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关键词
Training,Learning (artificial intelligence),Machine learning,Complexity theory,Training data,Games,Robustness
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