There are 2 repositories under sac topic.
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)
Python library for Reinforcement Learning.
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.
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.
🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2
JAX (Flax) implementation of algorithms for Deep Reinforcement Learning with continuous action spaces.
CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning
PyTorch implementation of deep reinforcement learning algorithms
PyTorch implementation of Soft Actor-Critic (SAC)
Reinforcement Learning Algorithms Based on PyTorch
DrQ: Data regularized Q
RAD: Reinforcement Learning with Augmented Data
lagom: A PyTorch infrastructure for rapid prototyping of reinforcement learning algorithms.
Master classic RL, deep RL, distributional RL, inverse RL, and more using OpenAI Gym and TensorFlow with extensive Math
Implementation of reinforcement learning approach to make a car learn to drive smoothly in minutes
Simple (but often Strong) Baselines for POMDPs in PyTorch, ICML 2022
PyTorch implementation of Soft-Actor-Critic and Prioritized Experience Replay (PER) + Emphasizing Recent Experience (ERE) + Munchausen RL + D2RL and parallel Environments.
Paddle-RLBooks is a reinforcement learning code study guide based on pure PaddlePaddle.
Provide full reinforcement learning benchmark on mujoco environments, including ddpg, sac, td3, pg, a2c, ppo, library
JAX implementation of deep RL agents with resets from the paper "The Primacy Bias in Deep Reinforcement Learning"
Implementations of Deep Reinforcement Learning Algorithms and Bench-marking with PyTorch
Deep Reinforcement Learning for Continuous Control in PyTorch
AI RC Car Agent that using deep reinforcement learning on Jetson Nano
Basic reinforcement learning algorithms. Including:DQN,Double DQN, Dueling DQN, SARSA, REINFORCE, baseline-REINFORCE, Actor-Critic,DDPG,DDPG for discrete action space, A2C, A3C, TD3, SAC, TRPO