• Introduction to Deep Reinforcement Learning
  • Introduction
  • Deep Learning
  • Reinforcement Learning
  • Deep Reinforcement Learning
  • Future
  • My thoughts
  • References
  • Guidelines
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References

相关资料

Parallel and Distributed Computation:Numerical Methods:

https://dspace.mit.edu/handle/1721.1/3719#files-areaOpenAI

Gym:

http://arxiv.org/pdf/1606.01540.pdfDQN

https://www.cs.toronto.edu/~vmnih/docs/dqn.pdfDouble

DQN

http://arxiv.org/pdf/1509.06461v3.pdfDPG

http://jmlr.org/proceedings/papers/v32/silver14.pdfDDPG

http://arxiv.org/abs/1509.02971GAE

http://arxiv.org/pdf/1506.02438v4Nervana:

http://www.nervanasys.com/demystifying-deep-reinforcement-learning/David

Silver drl:

http://videolectures.net/site/normal_dl/tag=968058/rldm2015_silver_reinforcement_learning.pdfJohn

Schulman drl:

http://rl-gym-doc.s3-website-us-west-2.amazonaws.com/mlss/2016-MLSS-RL.pdfReinforced

Variational Inference:

http://arkitus.com/files/nips-15-weber-reinforced-inference.pdfGradient

Estimation Using Stochastic Computation Graphs:

https://arxiv.org/pdf/1506.05254.pdfControl

of Memory, Active Perception, and Action in Minecraft:

http://arxiv.org/abs/1605.09128Deep

Reinforcement Learning in Large Discrete Action Spaces:

https://arxiv.org/pdf/1512.07679.pdfDeep

Reinforcement Learning from Self-Play in Imperfect-Information Games:

https://arxiv.org/pdf/1603.01121.pdfContinuous

control with deep reinforcement learning:

http://www0.cs.ucl.ac.uk/staff/d.silver/web/Publications_files/ddpg.pdf

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