There are 5 repositories under soft-actor-critic topic.
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 implementation of Soft Actor-Critic (SAC), Twin Delayed DDPG (TD3), Actor-Critic (AC/A2C), Proximal Policy Optimization (PPO), QT-Opt, PointNet..
PyTorch implementation of soft actor critic
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.
JAX (Flax) implementation of algorithms for Deep Reinforcement Learning with continuous action spaces.
PyTorch implementation of Soft Actor-Critic (SAC)
Reinforcement Learning for real-time applications - host of the TrackMania Roborace League
RAD: Reinforcement Learning with Augmented Data
lagom: A PyTorch infrastructure for rapid prototyping of reinforcement learning algorithms.
DeepRL algorithms implementation easy for understanding and reading with Pytorch and Tensorflow 2(DQN, REINFORCE, VPG, A2C, TRPO, PPO, DDPG, TD3, SAC)
Implementation of reinforcement learning approach to make a car learn to drive smoothly in minutes
PyTorch implementation of Soft-Actor-Critic and Prioritized Experience Replay (PER) + Emphasizing Recent Experience (ERE) + Munchausen RL + D2RL and parallel Environments.
PyTorch implementation of Soft Actor-Critic + Autoencoder(SAC+AE)
A pytorch tutorial for DRL(Deep Reinforcement Learning)
JAX implementation of deep RL agents with resets from the paper "The Primacy Bias in Deep Reinforcement Learning"
深度强化学习路径规划, SAC-Auto路径规划, Soft Actor-Critic算法, SAC-pytorch,激光雷达Lidar避障,激光雷达仿真模拟,Adaptive-SAC
Modified versions of the SAC algorithm from spinningup for discrete action spaces and image observations.
Implementation of Algorithms from the Policy Gradient Family. Currently includes: A2C, A3C, DDPG, TD3, SAC
Combining deep learning and reinforcement learning.
Proto-RL: Reinforcement Learning with Prototypical Representations
JAX implementations of core Deep RL algorithms
Collection of Deep Reinforcement Learning Algorithms implemented in PyTorch.
Deep Reinforcement Learning Framework for Manipulator based on NVIDIA's Isaac-gym, Additional add SAC2019 and Reinforcement Learning from Demonstration Algorithm.
Reinforcement Learning Agents in .NET
JAX code for the paper "Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation"
Single-file pytorch implementation of hybrid-SAC
Soft Actor-Critic with advanced features
PyTorch implementation of the discrete Soft-Actor-Critic algorithm.
This Repository contains a series of google colab notebooks which I created to help people dive into deep reinforcement learning.This notebooks contain both theory and implementation of different algorithms.
A Pytorch Implementation of Multi Agent Soft Actor Critic
DRL-based path planner for real quadrotor