This repository contains a series of tutorials on Deep Reinforcement Learning (DRL). This includes slides as well as experiments. Going forward I plan on adding exercises as well as complementary blog posts. So stay tuned!

• Slides: Includes DQN, Double DQN, Prioritized Experience Replay & Dueling DQNs
• Experiments: Provides code to implement all of the above.
• Blog Post I/II: Covering all algorithms from Fitted Q-Learning to Categorical DQNs.
• Replicating the experiments:

  1. Create & activate a virtual env. Install the requirements.

  2. Afterwards you can run all experiments by executing:

     time bash run_experiments_dqn.sh dqn <cuda_device_id>
     time bash run_experiments_dqn.sh double-dqn <cuda_device_id>
     time bash run_experiments_dqn.sh per-dqn <cuda_device_id>
     time bash run_experiments_dqn.sh dueling-dqn <cuda_device_id>
     

  3. The visualizations for the different experiments as well as the mini double DQN illustration can be replicated by executing the notebook:

     jupyter notebook viz_results.ipynb
     

  4. Finally, in order to visualize an episode rollout of a DQN agent at different stages do the following:

     python train_dqn.py --SAVE_AGENT
     python enjoy_dense.py --AGENT 5000_MLP-DQN --TITLE 5000
     python enjoy_dense.py --AGENT 40000_MLP-DQN --TITLE 40000
     python enjoy_dense.py --AGENT 500000_MLP-DQN --TITLE 500000