Notebooks and code for Alpha Architect post on reinforcement learning.
- Tic-Tac-Toe.ipynb - Table-based reinforcement learning to play Tic-Tac-Toe, and analogous if pointless deep learning algo
- Cart-Pole.ipynb - Building deep reinforcement learning algos from scratch with Keras for OpenAI environments like Cartpole and LunarLander.
- DQN
- Policy Gradient (REINFORCE)
- REINFORCE with baseline
- Run_CartPole.ipynb, Run_LunarLander.ipynb - only run saved good models, don't train
- Ray_tune.ipynb - Similar but with state of the art RL from UC Berkeley Ray project
- Trading_with_RL.ipynb - Algos to trade fake market data, inspired by Gordon Ritter paper Machine Learning for Trading. This should run in Google Colab.
Typical installation procedure:
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Install Anaconda python data science distribution
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Make an environment like
conda create --name tf tensorflow
or if you have Nvidia GPU
conda create --name tf_gpu tensorflow-gpu
This should install requirements like working Nvidia drivers
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Upgrade TensorFlow to latest version with
pip install --upgrade tensorflow
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Install additional requirements as necessary - requirements.txt has python modules installed at time of testing.
pip install -r requirements.txt
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TensorFlow Docker install may also be a good way to start but has not been tested.
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Run notebooks using
jupyter notebook