128 skills found · Page 1 of 5
sail-sg / EnvpoolC++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.
YeWR / EfficientZeroOpen-source codebase for EfficientZero, from "Mastering Atari Games with Limited Data" at NeurIPS 2021.
google-research / Batch RlOffline Reinforcement Learning (aka Batch Reinforcement Learning) on Atari 2600 games
JetSetIlly / Gopher2600Gopher2600 is an emulator for the Atari 2600 games console
gsurma / AtariAI research environment for the Atari 2600 games 🤖.
Nasdin / ReinforcementLearning AtariGamePytorch LSTM RNN for reinforcement learning to play Atari games from OpenAI Universe. We also use Google Deep Mind's Asynchronous Advantage Actor-Critic (A3C) Algorithm. This is much superior and efficient than DQN and obsoletes it. Can play on many games
johnidm / Asm Atari 2600Sample source code games Atari 2600
elleryqueenhomels / AI For AtariDeep Reinforcement Learning Algorithms for solving Atari 2600 Games
batari-Basic / Batari Basica BASIC-like language for creating games that run on the Atari 2600 console.
michaelnny / Deep Rl ZooA collection of Deep Reinforcement Learning algorithms implemented with PyTorch to solve Atari games and classic control tasks like CartPole, LunarLander, and MountainCar.
junhyukoh / Nips2015 Action Conditional Video PredictionImplementation of "Action-Conditional Video Prediction using Deep Networks in Atari Games"
k4ntz / OC AtariObject Centric Atari games
davide97l / Rl Policies Attacks DefensesAdversarial attacks on Deep Reinforcement Learning (RL)
jonathonbyrd / Deep Rl AleAn implementation of Deep Reinforcement Learning / Deep Q-Networks for Atari games in TensorFlow
eparisotto / ActorMimicTrain an RL agent to play multiple Atari games at once
TianhongDai / Self Imitation Learning PytorchThis is the pytorch implementation of ICML 2018 paper - Self-Imitation Learning.
danielegrattarola / Deep Q AtariKeras and OpenAI Gym implementation of the Deep Q-learning algorithm to play Atari games.
microsoft / FQFFQF(Fully parameterized Quantile Function for distributional reinforcement learning) is a general reinforcement learning framework for Atari games, which can learn to play Atari games automatically by predicting return distribution in the form of a fully parameterized quantile function.
dbobrenko / Async DeeprlPlaying Atari games with TensorFlow implementation of Asynchronous Deep Q-Learning
rohitgirdhar / Deep Q NetworksImplementation of Deep/Double Deep/Dueling Deep Q networks for playing Atari games using Keras and OpenAI gym
