An elegant PyTorch deep reinforcement learning library.
-
Updated
Dec 10, 2024 - Python
An elegant PyTorch deep reinforcement learning library.
PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....
Massively Parallel Deep Reinforcement Learning. 🔥
Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)
Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)
Modular Deep Reinforcement Learning framework in PyTorch. Companion library of the book "Foundations of Deep Reinforcement Learning".
Python library for Reinforcement Learning.
32 projects in the framework of Deep Reinforcement Learning algorithms: Q-learning, DQN, PPO, DDPG, TD3, SAC, A2C and others. Each project is provided with a detailed training log.
This repository contains most of pytorch implementation based classic deep reinforcement learning algorithms, including - DQN, DDQN, Dueling Network, DDPG, SAC, A2C, PPO, TRPO. (More algorithms are still in progress)
A PyTorch library for building deep reinforcement learning agents.
JAX (Flax) implementation of algorithms for Deep Reinforcement Learning with continuous action spaces.
🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2
CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning
PyTorch implementation of Soft Actor-Critic (SAC)
Reinforcement Learning Algorithms Based on PyTorch
DrQ: Data regularized Q
Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...
RAD: Reinforcement Learning with Augmented Data
Master classic RL, deep RL, distributional RL, inverse RL, and more using OpenAI Gym and TensorFlow with extensive Math
Add a description, image, and links to the sac topic page so that developers can more easily learn about it.
To associate your repository with the sac topic, visit your repo's landing page and select "manage topics."