A Pytorch Implementation of Multi Agent Soft Actor Critic
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Updated
Jan 29, 2019 - Jupyter Notebook
A Pytorch Implementation of Multi Agent Soft Actor Critic
Pytorch implementation of Soft Actor-Critic algorithm
Pytorch implementation of Hierarchical Intentional-Unintentional Soft Actor-Critic (HIU-SAC) algorithm
Tensorflow implementation of reinforcement learning (PG, A2C, DQN, DDPG, PPO, HER, SAC)
Implementation of Algorithms from the Policy Gradient Family. Currently includes: A2C, A3C, DDPG, TD3, SAC
Deep reinforcement learning project. We want to solve all the open AI gym problem.
PyTorch implementations of Reinforcement Learning algorithms in less than 200 lines
PyTorch implementation of Soft Actor-Critic + Autoencoder(SAC+AE)
Modified versions of the Soft Actor-Critic algorithm for Atari games from https://github.com/ac-93/soft-actor-critic.
Modified versions of the SAC algorithm from spinningup for discrete action spaces and image observations.
A JAX Implementation of the Soft Actor Critic Algorithm
PyTorch Implementation of Soft Actor-Critic Algorithm
meta-RL soft actor-critic with BRUNO for task inference
Soft Actor Critic Agent implementation in Python
Collection of Deep Reinforcement Learning Algorithms implemented in PyTorch.
Carla Multi Agent Deep Reinforcement Learning
Playing around with RL (mostly stand-alone RL methods)
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)
Soft Actor-Critic implementation with SOTA model-free extension (REDQ) and SOTA model-based extension (MBPO).
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