microsoft / FQF
FQF(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.
☆42Updated 4 years ago
Alternatives and similar repositories for FQF:
Users that are interested in FQF are comparing it to the libraries listed below
- Pytorch implementation of distributed deep reinforcement learning☆75Updated 2 years ago
- Datasets for data-driven deep reinforcement learning with PyBullet environments☆148Updated 4 years ago
- PyTorch implementation of FQF, IQN and QR-DQN.☆175Updated 8 months ago
- PyTorch implementation of the state-of-the-art distributional reinforcement learning algorithm Fully Parameterized Quantile Function (FQF…☆30Updated 4 years ago
- pytorch-implementation of Dreamer (Model-based Image RL Algorithm)☆165Updated 2 months ago
- PyTorch implementation of Stochastic Latent Actor-Critic(SLAC).☆89Updated 8 months ago
- Soft Actor-Critic☆144Updated 7 years ago
- Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model☆150Updated 4 years ago
- PyTorch implementation of Never Give Up: Learning Directed Exploration Strategies☆56Updated 4 years ago
- ☆31Updated 5 years ago
- Fast Flexible Replay Buffer Library (Mirror repository of https://gitlab.com/ymd_h/cpprb)☆72Updated 3 months ago
- Unofficial Pytorch code for "Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models"☆188Updated 2 years ago
- Revisiting Rainbow☆74Updated 3 years ago
- A Modular Library for Off-Policy Reinforcement Learning with a focus on SafeRL and distributed computing☆133Updated 8 months ago
- Recurrent and multi-process PyTorch implementation of deep reinforcement Actor-Critic algorithms A2C and PPO☆196Updated 2 years ago
- Implementation of Truncated Quantile Critics method for continuous reinforcement learning. https://bayesgroup.github.io/tqc/☆93Updated 4 years ago
- ☆194Updated 2 years ago
- Codes accompanying the paper "ROMA: Multi-Agent Reinforcement Learning with Emergent Roles" (ICML 2020 https://arxiv.org/abs/2003.08039)☆156Updated 2 years ago
- Modified versions of the SAC algorithm from spinningup for discrete action spaces and image observations.☆94Updated 4 years ago
- Code accompanying the paper "Better Exploration with Optimistic Actor Critic" (NeurIPS 2019)☆70Updated last year
- A Tensorflow implementation of the Option-Critic Architecture☆72Updated 7 years ago
- Keeping track of RL experiments☆162Updated 2 years ago
- OpenAI Gym wrapper for the DeepMind Control Suite☆213Updated 10 months ago
- Random network distillation on Montezuma's Revenge and Super Mario Bros.☆48Updated 2 years ago
- ☆298Updated 3 months ago
- Combining Evolutionary Algorithms and deep RL in various ways☆102Updated 4 years ago
- Arena: A General Evaluation Platform and Building Toolkit for Single/Multi-Agent Intelligence. AAAI 2020.☆83Updated 3 years ago
- ☆112Updated last year
- Code for "Data-Efficient Reinforcement Learning with Self-Predictive Representations"☆160Updated 3 years ago
- ☆53Updated last year