Reinforcement Learning
  • Fundamentals
  • Solve a known MDP - dynamic programming:
  • Model free Learning - Estimate the value function of an unknown MDP
  • Approximate Methods - Deep RL
  • Open Problems
  • Key Papers
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  • Reinforcement Basics:
  • Classic Papers:
  • Deep RL:

Key Papers

PreviousOpen Problems

Last updated 2 years ago

Deep Learning Theory: , FAST.AI (practical)

Reinforcement Basics:

  • Richard Sutton Book

  • Emma and stanford COurse:

  • David Silver Course:

Classic Papers:

  • MDPs:

  • TD-Learning:

  • Q-Learning:

  • REINFORCE:

  • Prioritized Sweeping:

  • Markov Games and RL:

  • Value function approximation:

  • Complexity of MDPs:

  • RL for Backgammon:

  • POMDPs:

  • Reward Shaping:

  • Policy Gradients:

  • Options/Temporal Abstractions:

  • Hierarchical RL:

  • Model-based RL with guarantees #1:

  • Model-based RL with guarantees #2:

  • Inverse RL:

Deep RL:

Deep RL David Silver Tutorial: watch NIPS keynote and and Survey Papers:

,

Critique:

https://stats385.github.io/readings
course
Bellman 1957
Sutton 1988
Watkins and Dayan 1992
Williams 1992
Moore and Atkeson 1993
Littman 1994
Boyan and Moore 1995
Littman, Dean, Kaelbling 1995
Tesauro 1995
Kaelbling, Littman, Cassandra 1998
Ng, Harada, and Russell 1999
Sutton, McAllaster, Singh, Mansour 2000
Sutton, Precup, Singh
Dietterich 2000
Kearns and Singh 2002
Braffman and Tennenholtz 2002
Abbeil and Ng 2004
DRL tutorial
DEEP REINFORCEMENT LEARNING: AN OVERVIEW
https://arxiv.org/pdf/1708.05866.pdf
A long peek into RL
Principles of deep RL:
https://www.alexirpan.com/2018/02/14/rl-hard.html