A2c Open Source Projects

An elegant PyTorch deep reinforcement learning library.

High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)

Learn Deep Reinforcement Learning in 60 days! Lectures & Code in Python. Reinforcement Learning + Deep Learning

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

Massively Parallel Deep Reinforcement Learning. 🔥

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

Implementations from the free course Deep Reinforcement Learning with Tensorflow and PyTorch

Modularized Implementation of Deep RL Algorithms in PyTorch

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

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

PyTorch implementation of Deep Reinforcement Learning: Policy Gradient methods (TRPO, PPO, A2C) and Generative Adversarial Imitation Learning (GAIL). Fast Fisher vector product TRPO.

Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch

XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library

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.

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

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.

PyTorch implementations of various Deep Reinforcement Learning (DRL) algorithms for both single agent and multi-agent.

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

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

PyTorch C++ Reinforcement Learning

PyTorch implementation of deep reinforcement learning algorithms

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

Reinforcement learning tutorials

Tutorials for reinforcement learning in PyTorch and Gym by implementing a few of the popular algorithms. [IN PROGRESS]

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

A pytorch tutorial for DRL(Deep Reinforcement Learning)

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

Code accompanying the blog post "Deep Reinforcement Learning with TensorFlow 2.1"

A Clearer and Simpler Synchronous Advantage Actor Critic (A2C) Implementation in TensorFlow

PyTorch implementation of some reinforcement learning algorithms: A2C, PPO, Behavioral Cloning from Observation (BCO), GAIL.

StarCraft II / PySC2 Deep Reinforcement Learning Agents (A2C)

A drone control system based on deep reinforcement learning with Tensorflow and ROS

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

Adversarial attacks on Deep Reinforcement Learning (RL)