Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学

PyTorch implementation of Asynchronous Advantage Actor Critic (A3C) from "Asynchronous Methods for Deep Reinforcement Learning".

Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow

Simple A3C implementation with pytorch + multiprocessing

A3C LSTM Atari with Pytorch plus A3G design

Pytorch LSTM RNN for reinforcement learning to play Atari games from OpenAI Universe. We also use Google Deep Mind's Asynchronous Advantage Actor-Critic (A3C) Algorithm. This is much superior and efficient than DQN and obsoletes it. Can play on many games

Deep reinforcement learning using an asynchronous advantage actor-critic (A3C) model.

Simple Reinforcement Learning Framework

Play Atari(Breakout) Game by DRL - DQN, Noisy DQN and A3C

Attentional Mechanism incorporated in Asynchronous Advantage Actor Critic a3c/a2c deep mind

PyTorch implementation of A3C (Asynchronous Advantage Actor Critic)

The Asynchronous Advantage Actor Critic (A3C) algorithm is one of the newest algorithms to be developed under the field of Deep Reinforcement Learning Algorithms. This algorithm was developed by Google’s DeepMind which is the Artificial Intelligence division of Google. In this repository, I have my implementations of A3C on Cartpole game, Robot …

Deep reinforcement learning agent

I utilized the A3C (Asynchronous Advantage Actor-Critic) algorithm to train a Deep Q-Learning (DQN) model, specifically tailored to solve the Kungfu gym environment.

StarCraft II / PySC2 reinforcement learning research