This repository hosts Jupyter notebooks showcasing the training of Atari games using a variety of Deep Reinforcement Learning (RL) algorithms such as Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), Deep Q-Networks (DQN), Advantage Actor-Critic (A2C), and more.

Asteroids Game in Javascript

C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.

A convolutional net, re-inforcement learning model that plays Atari's Breakout

This code implements an Asynchronous Advantage Actor-Critic (A3C) algorithm using PyTorch to train an agent to play the Atari game "Boxing"

The official implementation of MeDQN algorithm.

Implementation Deep Q Network to play Atari Games

⛷ DQN and DDQN algorithms for OpenAI Gym Skiing environment 🎮

Atari 8-bit Rom Image File Explorer based upon ATR Image Explorer

A simple memory game for Atari 2600

Tetris clone made with OSS Action! Language

Reinforcement Learning Projects - Breakout, Car Racing, Cart Pole

A brick breaker game in x86 assembly language

Implementation of the Space Race game, created by Atari.

Atari 2600 game made with Assembly 6502

This repository is the official implementation of the Hybrid Self-Attention NEAT algorithm. It contains the code to reproduce the results presented in the original paper: https://link.springer.com/article/10.1007/s12530-023-09510-3

A CLI tool to help train Atari games for performing experiments related to transfer learning in reinforcement learning built as part of our final year project.

Deep Q-learning with TensorFlow

Simple Pong AI that you cannot beat.