Reinforcement Learning, Fast and Slow

时间 2020-12-23

标签类脑强化学习繁體版

原文原文链接

Reinforcement Learning, Fast and Slow 摘要：深度强化学习已经取得很大成就，但是最大的缺陷在于样本数据的有效性低。主要有两种方法来解决这个问题： Episode Deep RL Meta RL 深度强化学习样本数据的有效性低的原因梯度下降。需要对参数进行迭代更新直到收敛。学习率不能太大否则无法收敛，学习率太小则收敛速度慢。弱偏置假设。机器学习模型都是要设定

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1. Reinforcement learning and Deep learning
2. Looking Fast and Slow: Memory-Guided Mobile Video Object Detection
3. Reinforcement learning: integrating learning and planning, exploitation and exploration
4. Reinforcement Learning Note: Concept and MDP
5. Fast deep reinforcement learning using online adjustments from the past
6. 《Distantly Supervised NER with Partial Annotation Learning and Reinforcement Learning》
7. Fast Slow RNN ——译文
8. 视频目标检测Looking Fast and Slow: Memory-Guided Mobile Video Object Detection
9. Deep Reinforcement Learning
10. RLChina_Lecture01_《Introduce to Reinforcement Learning and Value-based Methods》_notebook

>>更多相关文章<<