The repository contains lecture notes and self-coded solutions to various assignment problems from below course:
- Simple Reinforcement Learning with Tensorflow (10 Parts) - Arthur Juliani, 2016
- Reinforcement Learning I: Introduction - Richard Sutton et al., 1998
- The Theory of Dynamic Programming - Richard Bellman, 1954
- A Survey of Applications of Markov Decision Processes - D. J. White, 1993
- Markov Decision Processes: Concepts and Algorithms - Martijn van Otterlo, 2009
- Learning to Predict by the Methods of Temporal Differences - Richard Sutton, 1988
- Efficient Back Propogation - Yann LeCun et al., 1998
- Deep Sparse Rectifier Neural Networks - Xavier Glorot et al., 2011
- A List of Cost Functions Used In Neural Networks - CrossValidated, 2015
- A Neural Network in 13 lines of Python - Andrew Trask, 2015
- How the backpropagation algorithm works - Michael Nielsen, 2015
- Demystifying Deep Reinforcement Learning - Tambet Matiisen, 2015
- Prioritized Experience Replay - Tom Schaul et al., Google DeepMind, 2016
- Adaptive ε-greedy Exploration in Reinforcement Learning Based on Value Differences - Michel Tokic, 2010
- Reinforcement Learning: An Introduction - Richard S. Sutton and Andrew G. Barto, 1998
- Asynchronous Methods for Deep Reinforcement Learning - Volodymyr Mnih et al., 2016
- Let’s Make An A3c: Implementation - Jaromír Janisch, 2017
- High-dimensional Continuous Control Using Generalized Advantage Estimation - John Schulman et al., 2016
- Artificial Intelligence A-Z Handbook – Learn How to Build An A.I - Hadelin de Ponteves and Kirill Eremenko
- Q-Learning Visualization - ai.berkeley.edu