论文笔记 Benchmarking Model-Based Reinforcement Learning
摘要 Model-based强化学习（MBRL）比Model-free强化学习（MFRL）更加sample efficient。目前对于MBRL的研究没有标准，作者们可能会使用自己设计的环境、一些闭源且不能复现的结果。本文收集了大批MBRL算法，并在18个为MBRL特别设计的基准环境上按照统一的设置运行。本文还描述了三个未来MBRL研究的关键挑战：动力瓶颈、规划周期困境、提前终止困境、 介绍
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相关文章
- 1. 论文笔记——NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING
- 2. 论文笔记《Neural Architecture Search With Reinforcement Learning》
- 3. 【论文笔记】NEURAL COMBINATORIAL OPTIMIZATION WITH REINFORCEMENT LEARNING
- 4. 论文笔记：《Playing Atari with Deep Reinforcement Learning》
- 5. 论文笔记 Reinforcement Learning with Derivative-Free Exploration
- 6. 论文笔记：Learning how to Active Learn: A Deep Reinforcement Learning Approach
- 7. 论文笔记4：Language Understanding for Text-based Games using Deep Reinforcement Learning
- 8. 论文笔记 Cross-modal Bidirectional Translation via Reinforcement Learning
- 9. 【论文笔记】 Reinforcement-Learning-Guided Source Code Summarization using Hierarchical Attention
- 10. 论文阅读笔记（五十二）：Outline Objects using Deep Reinforcement Learning