Model parameters training with i-DQN and i-IQN
This repository contains the model parameters trained with i-DQN on $57$ Atari games and trained with i-IQN on $20$ Atari games 🎮. $5$ seeds are available for each configuration which makes a total of $385$ available models 📈.
The evaluate.ipynb notebook contains a minimal example to evaluate to model parameters 🧑🏫. It uses JAX 🚀.
ps: The set of $20$ Atari games is included in the set of $57$ Atari games.
Model performances
List of games for i-DQN
Alien, Amidar, Assault, Asterix, Asteroids, Atlantis, BankHeist, BattleZone, BeamRider, Berzerk, Bowling, Boxing, Breakout, Centipede, ChopperCommand, CrazyClimber, DemonAttack, DoubleDunk, Enduro, FishingDerby, Freeway, Frostbite, Gopher, Gravitar, Hero, IceHockey, Jamesbond, Kangaroo, Krull, KungFuMaster, MontezumaRevenge, MsPacman, NameThisGame, Phoenix, Pitfall, Pong, Pooyan, PrivateEye, Qbert, Riverraid, RoadRunner, Robotank, Seaquest, Skiing, Solaris, SpaceInvaders, StarGunner, Tennis, TimePilot, Tutankham, UpNDown, Venture, VideoPinball, WizardOfWor, YarsRevenge, Zaxxon.
List of games for i-IQN
Alien, Assault, BankHeist, Berzerk, Breakout, Centipede, ChopperCommand, DemonAttack, Enduro, Frostbite, Gopher, Gravitar, IceHockey, Jamesbond, Krull, KungFuMaster, Riverraid, Seaquest, Skiing, StarGunner.
User installation
Python 3.10 is recommended. Create a Python virtual environment, activate it, update pip and install the package and its dependencies in editable mode:
python3.10 -m venv env
source env/bin/activate
pip install --upgrade pip
pip install numpy==1.23.5 # to avoid numpy==2.XX
pip install -r requirements.txt
pip install --upgrade "jax[cuda12_pip]==0.4.13" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
Citing i-QN
@article{vincent2024iterated,
title={Iterated $ Q $-Network: Beyond the One-Step Bellman Operator},
author={Vincent, Th{\'e}o and Palenicek, Daniel and Belousov, Boris and Peters, Jan and D'Eramo, Carlo},
journal={arXiv preprint arXiv:2403.02107},
year={2024}
}