There are 5 repositories under advantage-actor-critic topic.
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).
Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch
A PyTorch library for building deep reinforcement learning agents.
PyTorch C++ Reinforcement Learning
PyTorch implementations of various Deep Reinforcement Learning (DRL) algorithms for both single agent and multi-agent.
Code for Hands On Intelligent Agents with OpenAI Gym book to get started and learn to build deep reinforcement learning agents using PyTorch
Tutorials for reinforcement learning in PyTorch and Gym by implementing a few of the popular algorithms. [IN PROGRESS]
Code accompanying the blog post "Deep Reinforcement Learning with TensorFlow 2.1"
Recurrent and multi-process PyTorch implementation of deep reinforcement Actor-Critic algorithms A2C and PPO
PyTorch implementation of some reinforcement learning algorithms: A2C, PPO, Behavioral Cloning from Observation (BCO), GAIL.
Curiosity-driven Exploration by Self-supervised Prediction
Reinforcing Your Learning of Reinforcement Learning
A well-documented A2C written in PyTorch
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
MLP-framework (pure numpy) and DDQN-framework for OpenAI's Gym games. +test code for PPO added. +Hindsight Experience Replay(HER) bitflip-DQN example. +prioritized replay.
The friendly robot that beats you in Yahtzee 🤖 🎲
Official implementation of the AAAI 2021 paper Deep Bayesian Quadrature Policy Optimization.
Deep reinforcement learning package for torch7
It's a Raspberry Pi Pokémon that gamifies WiFi Hacking by learning from its surrounding WiFi environment utilising deep Reinforcement Learning.
Code for Hands On Intelligent Agents with OpenAI Gym book to get started and learn to build deep reinforcement learning agents using PyTorch
Solving CartPole-v1 environment in Keras with Advantage Actor Critic (A2C) algorithm an Deep Reinforcement Learning algorithm
Training a Reinforcement Learning Agent to Play Flappy Bird.
The pytorch implemetation of a2c
First Place Reinforcement Learning solution code and a writeup for the AI RoboSoccer Competition.
RL agent that is trained to catch moving pucks in a complex environment, with DQN, AC, A2C and more.
This repository contains my assignment solutions for the Deep Learning course (M2177.003100_002) offered by Seoul National University (Fall 2019).
Example A2C implementation with ReLAx
Multi-task learning with Advantage Actor Critic and sharing experience