Deep Q network implementation in Tensorflow

Shallow Reinforcement Learning on Atari

A collection of Deep Reinforcement Learning algorithms implemented in tensorflow. Very extensible. High performing DQN implementation.

Deep Q Learning

implementation of deep reinforcement learning algos

👾 My solutions to OpenAI Gym Reinforcement Learning problems.

I used Cross Entropy methods as an alternative to reinforcement learning methods to search policy space for the optimal policy where there is no assumption about the structure of the problem for both continuous and discrete space tasks in OpenAI gym.

An AI that plays Atari 2600 Pong. Trained with reinforcement learning using OpenAI Gym and Keras

Pytorch implementation of the Persistent Advantage reinforcement learning operator proposed in paper 'Increasing the Action Gap: New Operators for Reinforcement Learning'. This repository includes source code for training, demo, action gap visualization and trained models for 5 Atari games.

Pytorch implementation of the Persistent Advantage reinforcement learning operator proposed in paper 'Increasing the Action Gap: New Operators for Reinforcement Learning'

Tensorflow implementation of Reinforcement Learning methods for Atari 2600.

Implementation of DQN, DDQN and DRQNs for Atari Games in Tensorflow. [Work in Progress]

Rainbow, IQN on atari games

🍻 A simple implementation of dqn algorithm using pytorch

Deep reinforcement learning for self made atari game environment

A PyTorch implementation of reinforcement lerning algorithms (DQN, DDQN, Prior DDQN, Distributed) based on ray

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 deep-q reinforcement learning agent built from the ground up in Python to play Atari 2600 games based on the work published by DeepMind.

Playfield Pixel Perfect Pro: The ultimate Atari 2600 playfield designer