Deep_control
Deep Reinforcement Learning for Continuous Control in PyTorch
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Stock Prediction Models | 6,233 | a year ago | 46 | apache-2.0 | Jupyter Notebook | |||||
| Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations | ||||||||||
| Deeprl | 2,834 | 2 years ago | 5 | mit | Python | |||||
| Modularized Implementation of Deep RL Algorithms in PyTorch | ||||||||||
| Deep Reinforcement Learning With Pytorch | 2,741 | a year ago | 26 | mit | Python | |||||
| PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and .... | ||||||||||
| Iccv2019 Learningtopaint | 2,125 | 2 years ago | mit | Python | ||||||
| ICCV2019 - Learning to Paint With Model-based Deep Reinforcement Learning | ||||||||||
| Softlearning | 1,108 | 5 months ago | 53 | other | Python | |||||
| Softlearning is a reinforcement learning framework for training maximum entropy policies in continuous domains. Includes the official implementation of the Soft Actor-Critic algorithm. | ||||||||||
| Pytorch A3c | 768 | 5 years ago | 17 | mit | Python | |||||
| PyTorch implementation of Asynchronous Advantage Actor Critic (A3C) from "Asynchronous Methods for Deep Reinforcement Learning". | ||||||||||
| Super Mario Bros A3c Pytorch | 735 | 3 years ago | 10 | mit | Python | |||||
| Asynchronous Advantage Actor-Critic (A3C) algorithm for Super Mario Bros | ||||||||||
| Gdrl | 422 | 2 years ago | bsd-3-clause | Jupyter Notebook | ||||||
| Grokking Deep Reinforcement Learning | ||||||||||
| Tf2rl | 408 | 2 years ago | 23 | June 18, 2021 | 36 | mit | Python | |||
| TensorFlow2 Reinforcement Learning | ||||||||||
| Genrl | 375 | 2 years ago | 4 | March 31, 2020 | 52 | mit | Python | |||
| A PyTorch reinforcement learning library for generalizable and reproducible algorithm implementations with an aim to improve accessibility in RL | ||||||||||
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