Reinforcement Learning interactive examples
A number of jupyter notebooks introducing basic reinforcement learning algorithms applied on toy models. Algorithms implementations, notations and terms follow "Reinforcement learning - An introduction" book.
Requirements
- numpy/scipy
- matplotlib
- seaborn
- jupyter-notebook
Content
- Bridge crossing problem
- Dynamic Programming algorithms (policy iteration, value iteration)
- Monte Carlo methods (first-visit policy evalution, exploring starts, on-policy first-visit)
- Temporal Difference algorithms (SARSA, Q-learning)
- Racecar problem
- Monte Carlo Exploring Starts