Reinforcement Learning Examples

In each file, there is a jupyter notebook and its corresponding python file. Where in the note book records all the intermediate process and plots, in the python file is the compiled process you can run directly.

• Determnistic Grid World with Value Iteration

• Non-Deterministic Grid World with Q-Learning

• Tic-Tac-Toe Python Implementation

• Muilti-Arm Bandit Implementation

• Monte Carlo Simulation on Blackjack

• Blackjack Solution

• Cliff Walking Implementation

• Random Walk Implementation

• DynaMaze Implementation
• DynaMaze Extensions Implementation

• 1000-State Random Walk

• Tile Coding Implementation

• On-policy Function Approximation

• Generalisation on Continuing Tasks

• Generalisation of Off-Policy Learning

• Random Walk offline-Lambda
• Random Walk TD(\lambda)

• Sarsa-Lambda

• Short-Corridor