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

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An AI designed to run Atari games using Q-Learning. Based on paper 'Playing Atari with Deep Reinforcement Learning' by Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller.

BreakoutAI is an exciting project dedicated to conquering the classic Atari Breakout game through the power of reinforcement learning. Leveraging the state-of-the-art Stable Baselines3 library, our AI agent, armed with a Deep Q-Network (DQN), undergoes intense training sessions to master the art of demolishing bricks.

Develop an AI player of Atari Skiing with deep reinforcement learning

Use Reinforment Learning writting a Python program to win at Atari Pong with Open AI Gym

Pure JavaScript Chess Engine inspired by Atari Video Chess - A divide-and-conquer implementation with AI opponent, built for collaborative development by AI agents

In this project, we created AI agents in gym atari and retro-atari pong environment. We implement it combined with a convolutional neural network (CNN). And we conducted some experiments, analysis the result, and compare the pong environment between the gym and gym-retro platform to test our model.

We introduce the first deep reinforcement learning agent that learns to beat Atari games with the aid of natural language instructions. The agent uses a multimodal embedding between environment observations and natural language to self-monitor progress through a list of English instructions, granting itself additional reward for completing instructions in addition to increasing the game score. Our agent significantly outperforms Deep-Q Networks, Asynchronous Advantage Actor-Critic (A3C) agents, and the best agents posted to Open-AI Gym on what is often considered the hardest Atari 2600 environment: MONTEZUMA’S REVENGE.

David Silver FRS (born 1976) leads the reinforcement learning research group at DeepMind and was lead researcher on AlphaGo, AlphaZero and co-lead on AlphaStar. He graduated from Cambridge University in 1997 with the Addison-Wesley award, and befriended Demis Hassabis whilst there.[1] Subsequently, Silver co-founded the video games company Elixir Studios, where he was CTO and lead programmer, receiving several awards for technology and innovation.[1][2] Silver returned to academia in 2004 at the University of Alberta to study for a PhD on reinforcement learning, where he co-introduced the algorithms used in the first master-level 9×9 Go programs.[3][4] His version of program MoGo (co-authored with Sylvain Gelly) was one of the strongest Go programs as of 2009.[5] Silver was awarded a Royal Society University Research Fellowship in 2011, and subsequently became a lecturer at University College London, where he is now a professor.[6] His lectures on Reinforcement Learning are available on YouTube.[7] Silver consulted for DeepMind from its inception, joining full-time in 2013. His recent work has focused on combining reinforcement learning with deep learning, including a program that learns to play Atari games directly from pixels.[8] Silver led the AlphaGo project, culminating in the first program to defeat a top professional player in the full-size game of Go.[9] AlphaGo subsequently received an honorary 9 Dan Professional Certification; and won the Cannes Lion award for innovation.[10] He then led development of AlphaZero, which used the same AI to learn to play Go from scratch (learning only by playing itself and not from human games) before learning to play chess and shogi in the same way, to higher levels than any other computer program. Silver is among the most published members of staff at DeepMind, with over 67,000 citations and has an h-index of 66.[11] He was awarded the 2019 ACM Prize in Computing for breakthrough advances in computer game-playing.[12] In 2021, Silver was elected Fellow of the Royal Society for his contributions to Deep Q-Networks and AlphaGo.

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Play Atari Game with AI

My experiments with Atari AI

Beating atari breakout with ai

Atari breakout with simple AI agent

pong atari game with AI (reinforcement learning algorithm)

Implementation of Atari 2600 Video Chess, but with AI

An Atari Breakout clone written in Unity with AI opponents

Reinforcement Learning / AI to play Atari Breakout game with DQN algorithm.

Getting an AI to play Atari Pong, with deep reinforcement learning

Space invaders and other atari gameplay with reinforcement learning using Open AI

Atari Breakout clone built with Pygame featuring random level generation and AI

Atari Breakout game with simple AI built for my Computer Science NEA

An keras implementation for open ai gym atari games, open with jupyter.

AI agent that plays the Atari Centipede game. Trained with DQN (RL).

This is an Unity Ai Project with Atari breakout game developed and AI trained on it

An AI agent trained with deep reinforcement learning to play Atari Space Invaders

An AI that plays Atari Go based on the Minimax algorithm with alpha beta pruning.

A game of atari breakout played by an AI which keeps improving itself with every game.

Kebab Tycoon, an offline game in Atari 2600 graphic style made from ai with one prompt