Deep reinforcement learning package for torch7
-
Updated
Sep 17, 2016 - Lua
Deep reinforcement learning package for torch7
PyTorch implementations of various Deep Reinforcement Learning (DRL) algorithms for both single agent and multi-agent.
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.
Code for Hands On Intelligent Agents with OpenAI Gym book to get started and learn to build deep reinforcement learning agents using PyTorch
Multi-task learning with Advantage Actor Critic and sharing experience
A well-documented A2C written in PyTorch
Reinforcing Your Learning of Reinforcement Learning
PyTorch implementation of Asynchronous (and Synchronous) Advantage Actor Critic
RL agent that is trained to catch moving pucks in a complex environment, with DQN, AC, A2C and more.
PyTorch C++ Reinforcement Learning
Solving CartPole-v1 environment in Keras with Advantage Actor Critic (A2C) algorithm an Deep Reinforcement Learning algorithm
It's a Raspberry Pi Pokémon that gamifies WiFi Hacking by learning from its surrounding WiFi environment utilising deep Reinforcement Learning.
Tutorials for reinforcement learning in PyTorch and Gym by implementing a few of the popular algorithms. [IN PROGRESS]
Official implementation of the AAAI 2021 paper Deep Bayesian Quadrature Policy Optimization.
The friendly robot that beats you in Yahtzee 🤖 🎲
First Place Reinforcement Learning solution code and a writeup for the AI RoboSoccer Competition.
Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch
Code accompanying the blog post "Deep Reinforcement Learning with TensorFlow 2.1"
Add a description, image, and links to the advantage-actor-critic topic page so that developers can more easily learn about it.
To associate your repository with the advantage-actor-critic topic, visit your repo's landing page and select "manage topics."