50 Open source projects that are alternatives of or similar to Street-fighter-A3C-ICM-pytorch

TensorFlow A2C to solve Acrobot, with synchronized parallel environments

Random Network Distillation pytorch

Reinforcement Learning for Super Mario Bros using A3C on GPU

Master classic RL, deep RL, distributional RL, inverse RL, and more using OpenAI Gym and TensorFlow with extensive Math

Minimal implementations of reinforcement learning algorithms by Tensorflow

Combining deep learning and reinforcement learning.

Deep Reinforcement Learning A3C Crypto Trading Bot

Proximal Policy Optimization(PPO) with Intrinsic Curiosity Module(ICM)

LaTeX template, Outstanding papers of last years and some useful books about MATLAB

Minimal and Clean Reinforcement Learning Examples

A continuous action space version of A3C LSTM in pytorch plus A3G design

A library for ready-made reinforcement learning agents and reusable components for neat prototyping

Implementations of deep RL papers and random experimentation

Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)

Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...

A high-performance Atari A3C agent in 180 lines of PyTorch

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

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

强化学习中文教程，在线阅读地址：https://datawhalechina.github.io/easy-rl/

PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....

Reinforcement learning tutorials

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

Implementation of Algorithms from the Policy Gradient Family. Currently includes: A2C, A3C, DDPG, TD3, SAC

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

MXNET + OpenAI Gym implementation of A3C from "Asynchronous Methods for Deep Reinforcement Learning"

Modular Deep Reinforcement Learning framework in PyTorch. Companion library of the book "Foundations of Deep Reinforcement Learning".

My experimentations with Reinforcement Learning in Pytorch

Deep Learning and Reinforcement Learning Library for Scientists and Engineers 🔥

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

Scalable, event-driven, deep-learning-friendly backtesting library

Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学

Deep Reinforcement Learning with pytorch & visdom

Asynchronous Methods for Deep Reinforcement Learning

A3C LSTM Atari with Pytorch plus A3G design

Reinforcement Learning Tutorial with Demo: DP (Policy and Value Iteration), Monte Carlo, TD Learning (SARSA, QLearning), Function Approximation, Policy Gradient, DQN, Imitation, Meta Learning, Papers, Courses, etc..

Keras Implementation of popular Deep RL Algorithms (A3C, DDQN, DDPG, Dueling DDQN)

Simple A3C implementation with pytorch + multiprocessing

Accompanying repository for Let's make a DQN / A3C series.

Deep Reinforcement Learning for the JVM (Deep-Q, A3C)

RL starter files in order to immediatly train, visualize and evaluate an agent without writing any line of code

🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2

This is a simple implementation of DeepMind's PySC2 RL agents.

StarCraft II / PySC2 Deep Reinforcement Learning Agents (A2C)

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.

PyTorch implementation of "Asynchronous advantage actor-critic"

Tensorflow implementation of A3C algorithm

用深度强化学习玩合成大西瓜

Deep Reinforcement Learning in Autonomous Driving: the A3C algorithm used to make a car learn to drive in TORCS; Python 3.5, Tensorflow, tensorboard, numpy, gym-torcs, ubuntu, latex

pytorch implementation of Curiosity-driven Exploration by Self-supervised Prediction

Customisable Unified Physical Simulations (CUPS) for Reinforcement Learning. Experiments run on the ai2thor environment (http://ai2thor.allenai.org/) e.g. using A3C, RainbowDQN and A3C_GA (Gated Attention multi-modal fusion) for Task-Oriented Language Grounding (tasks specified by natural language instructions) e.g. "Pick up the Cup or else"