Implementation of Reinforcement Learning algorithms in Python, based on Sutton's & Barto's Book (Ed. 2)
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May 3, 2020 - Jupyter Notebook
Implementation of Reinforcement Learning algorithms in Python, based on Sutton's & Barto's Book (Ed. 2)
Exercise Solutions for Reinforcement Learning: An Introduction [2nd Edition]
📖Learning reinforcement learning by implementing the algorithms from reinforcement learning an introduction
Jupyter notebook containing a solution to Sutton and Barto's gridworld problem with both a random agent and a Q-learning agent.
Implementation of various Reinforcement Learning Algorithms
This is a Python implementation of concepts and algorithms described in "Reinforcement Learning: An Introduction" (Sutton and Barto, 2018, 2nd edition).
Reinforcement Learning Introduction - Selected Exercise Solutions & Experiment Code
Q-Learing algorithm solves simple mazes.
Selected algorithms and exercises from the book Sutton, R. S. & Barton, A.: Reinforcement Learning: An Introduction. 2nd Edition, MIT Press, Cambridge, 2018.
Code for the reading group on Sutton & Barto: Reinforcement Learning
"Learning to Predict by the Methods of Temporal Differences" by Sutton, Richard S. (1988)
Reinforcement Learning
My solutions to Sutton and Barto's book 'Reinforcement Learning: An Introduction'
Reinforcement Learning Course from IPVS
Reinforcement Learning Algorithms implemented based on pseudocode from Sutton and Barto
Implementations of RL Algos and solved exercises for Sutton&Barto RLAI
Train an AI to drive on a simple racetrack, by using reinforcement learning with Q-Learning and Monte Carlo. Inspired by Sutton and Barto's book.
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