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

An implementation of Deep Q-Learning from Demonstrations (DQfD) for playing Atari 2600 video games

JAX implementation of deep RL agents with resets from the paper "The Primacy Bias in Deep Reinforcement Learning"

👾 My solutions to OpenAI Gym Reinforcement Learning problems.

The code for the famous DQN paper applied on Atari's Breakout.

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

🍻 A simple implementation of dqn algorithm using pytorch

An implementation of a Deep Reinforcement Learning model that trains an agent to play Atari 2600 games.

Shallow Reinforcement Learning on Atari

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.

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

Playfield Pixel Perfect Pro: The ultimate Atari 2600 playfield designer

implementation of deep reinforcement learning algos

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.

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

Deep Q Learning

Code from "How useful is quantilization for mitigating specification-gaming?"

Q-learning script that can learn to play any Atari game using open AI gym.