Based on the book --- Reinforcement Learning: An Introduction (2nd ed, 2018) by Sutton and Barto
For the Reinforcement Learning course Assignment 2 (see 'Gridworld Problem 1.pdf') at Memorial University of Newfoundland, Jul. 18, 2024
For Gridworld Problem 2, see https://github.com/zyxsjdy/Solve-the-Gridworld-Problem-with-Reinforcement-Learning-Cont
python 3.11.9
Packages: numpy, cvxpy, copy
Use Jupyter Notebook to open the '.ipynb' file, and click Run All to run the file.
'P1.ipynb' for part 1, 'P2.ipynb' for part 2, and 'environment.py' for the gridworld environment.
See explanation for results in 'Report.pdf'.
Reinforcement Learning
Gridworld problem
Part 1:
Bellman equation
Bellman optimality equation
Iterative policy evaluation
Value iteration
Policy iteration
Policy improvement
Part 2:
Monte Carlo method
On-policy Monte Carlo ES (exploring starts)
On-policy first-visit Monte Carlo control for ϵ-soft policy
Off-policy Monte Carlo control
Off-policy Monte Carlo prediction (policy evaluation)