- Thesis title : Continuous Control With Deep Reinforcement Learning
The problem solved?
This article will be Deep Q-Learningapplied to the Deterministic Policy Gradientalgorithm. If you know DPG, then this article is to introduce DQNimproved a bit DPGof state value function. It solves the limitation of the DQNneed to find maximizes action-valueonly the discrete action space.
background
In fact, it is a combination of these two articles:
- 【5分钟 Paper】Playing Atari with Deep Reinforcement Learning
- 【5分钟 Paper】Deterministic Policy Gradient Algorithms
The method used?
This DDPGI too familiar, I really do not want to write what the appendix it a pseudo-code:
The effect achieved?
The experimental results are shown below:
Information published? author information?
This article is the one ICLR2016above. The first author TimothyP.Lillicrapis Google DeepMindof research Scientist.
Research focuses on machine learning and statistics for optimal control and decision making, as well as using these mathematical frameworks to understand how the brain learns. In recent work, I’ve developed new algorithms and approaches for exploiting deep neural networks in the context of reinforcement learning, and new recurrent memory architectures for one-shot learning. Applications of this work include approaches for recognizing images from a single example, visual question answering, deep learning for robotics problems, and playing games such as Go and StarCraft. I’m also fascinated by the development of deep network models that might shed light on how robust feedback control laws are learned and employed by the central nervous system.
- Personal homepage: http://contrastiveconvergence.net/~timothylillicrap/index.php
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