”actor-critic-algorithm“ 的搜索结果

Asynchronous Advantage Actor-Critic (A3C) algorithm for Super Mario Bros

Code for the MADDPG algorithm from the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"

Reinforcement learning baseline agent trained with the Actor-critic (A3C) algorithm.

The source code for "An Actor Critic Algorithm for Structured Prediction"

This repository contains the code to implement the Hierarchical Actor-Critic (HAC) algorithm.

Recurrent and multi-process PyTorch implementation of deep reinforcement Actor-Critic algorithms A2C and PPO

Softlearning is a reinforcement learning framework for training maximum entropy policies in continuous domains. Includes the official implementation of the Soft Actor-Critic algorithm.

Advantage async actor-critic Algorithms (A3C) and Progressive Neural Network implemented by tensorflow.

PyTorch implementation of Advantage async actor-critic Algorithms (A3C) in PyTorch

Pytorch implementation of the MARL algorithm, MADDPG, which correspondings to the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments".

Soft Actor-Critic

Actor-critic with experience replay

PyTorch implementation of soft actor critic

PyTorch implementation of Soft Actor-Critic (SAC)

Trading with recurrent actor-critic reinforcement learning

DSAC-v2; DASC; Distributional Soft Actor-Critic

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 Advantage Actor-Critic (A2C)

PyTorch implementation of Soft Actor-Critic + Autoencoder(SAC+AE)

General implementation of Advantage Actor Critic using Pytorch

advantage actor-critic reinforcement learning for openai gym cartpole

Code for "Actor-Attention-Critic for Multi-Agent Reinforcement Learning" ICML 2019

Using deep actor-critic model to learn best strategies in pair trading