I'm implementing algorithms from this book (Mathematical Foundation of Reinforcement Learning). I used the GridWorld environment code provided by the author and made a few changes.
- Chapter 4
- Value Iteration
- Policy Iteration
- Truncated Policy Iteration
- Chapter 5
- MC Basic
- MC Exploring Starts
- MC
$\epsilon$ -greedy
- Chapter 6
- RM Algorithm example
- Chapter 7
- Sarsa
- Q-learning (on-policy version)
- Q-learning (off-policy version)
It is easy to run the code by following the steps.
- activate your venv
conda activate base
# or
python -m venv venv
. venv/bin/activate
- install packages
pip install -r requirements.txt
- clone this repo
git clone [email protected]:co-gy/rl-learn.git
- run
make sure your running directory is rl-learn
cd rl-learn
python algorithm/value_iteration.py