This is a PyTorch implementation of Fully parameterized Quantile Function(FQF)[1], Implicit Quantile Networks(IQN)[2] and Quantile Regression DQN(QR-DQN)[3]. I tried to make it easy for readers to understand algorithms. Please let me know if you have any questions. Also, any pull requests are welcomed.
UPDATE
- 2020.6.9
- Bump torch up to 1.5.0.
- 2020.5.10
- Refactor codes.
- Fix Prioritized Experience Replay and Noisy Networks.
- Test IQN with Rainbow's components.
- 2020.6.9
- Bump Torch up to 1.5.0.
If you are using Anaconda, first create the virtual environment.
conda create -n fqf python=3.8 -y
conda activate fqfYou can install Python liblaries using pip.
pip install --upgrade pip
pip install -r requirements.txtIf you're using other than CUDA 10.2, you may need to install PyTorch. See instructions for more details.
You can train FQF agent using hyperparameters here.
python train_fqf.py --cuda --env_id PongNoFrameskip-v4 --seed 0 --config config/fqf.yaml
You can also train IQN or QR-DQN agent in the same way. Note that we log results with the number of frames, which equals to the number of agent's steps multiplied by 4 (e.g. 100M frames means 25M agent's steps).
Results of examples (without n-step rewards, double q-learning, dueling network nor noisy net) are shown below, which is comparable (if no better) with the paper. Scores below are evaluated arfer every 1M frames (250k agent's steps). Result are averaged over 2 seeds and visualized with min/max.
Note that I reported the "mean" score, not the "best" score as in the paper. Also, I only trained a limited number of frames due to limited resources (e.g. 100M frames instead of 200M).
I tested FQF, IQN and QR-DQN on BreakoutNoFrameskip-v4 for 30M frames to see algorithms worked.
I also tested FQF and IQN on BerzerkNoFrameskip-v4 for 100M frames to see the difference between FQF's performance and IQN's, which is quite obvious on this task.
I also tested IQN with Rainbow's components on PongNoFrameskip-v4 (just 1 seed). Note that I decreased num_steps to 7500000(30M frames), but kept start_steps as the same.
- Implement risk-averse policies for IQN.
- Test FQF-Rainbow agent.
[1] Yang, Derek, et al. "Fully Parameterized Quantile Function for Distributional Reinforcement Learning." Advances in Neural Information Processing Systems. 2019.
[2] Dabney, Will, et al. "Implicit quantile networks for distributional reinforcement learning." arXiv preprint. 2018.
[3] Dabney, Will, et al. "Distributional reinforcement learning with quantile regression." Thirty-Second AAAI Conference on Artificial Intelligence. 2018.