Markov Decision Process and Reinforcement Learning

Presentation Summary

In these slides, I present:

• Basic concepts of Markov Decision Process, Bellman equation
• Statistical mechanics of Markov Decision Process
• Soft Q-learning in reinforcement learning

Examples

• A well-documented code repo from OpenAI.

References

• Most contents here are from lectures in ICTP Spring College on Physics of Complex Systems, 2018. Full video lectures are available on ICTP Youtube channel and ICTP website.
• A good video lecture from Stanford CS231n course. I also strongly recommend the full course.
• Mnih, Volodymyr, et al. “Human-level control through deep reinforcement learning.” Nature 518.7540 (2015): 529.
• Sutton, Richard S., and Andrew G. Barto. Reinforcement learning: An introduction. MIT press, 2018.
• Deep reinforcement learning resources on OpenAI

All Machine Learning

• by Will Wei · Deep Learning Partial Differential Equation (PDE)
• by Will Wei · Adaptive Boosting
• by Zeqian Li · Markov Decision Process and Reinforcement Learning
• by Yubo 'Paul' Yang · Kriging
• by Dima Kochkov · Generative Adversarial Networks
• by Xiongjie Yu · Neural Network on a Tensor Train
• by Will Wheeler · Reservoir Computing in the Time Domain
• by Yubo 'Paul' Yang · Gibbs Sampling
• by Matt Zhang · Predict Seizure with EEG
• by Matt Zhang · Adaptive Boosting (AdaBoost)
• by Benjamin Villalonga Correa · Introduction to Supervised Machine Learning
• by Dima Kochkov · Boltzmann Machines