Train a reinforcement learning agent to play a variation of Pong®

FeDQN is a federated pipeline for training reinforcement learning agent of atari game, the Pong, developed during my first semester at Istanbul Technical University Computer Engineering MSc Program

Deep Reinforcement Learning Pong Agent, King Pong, he's the best

Trains an agent with (stochastic) Policy Gradients(actor-critic) on Pong. Uses OpenAI Gym.

A reimplementation of Andrej Karpathy's repository for an RL self-learning AI agent that learns to play Pong through trial and error, using PyTorch

Code for an intro to RL workshop. You'll be training a simple agent to play pong using policy gradients. Adapted from http://karpathy.github.io/2016/05/31/rl/

This project explores a deep reinforcement learning technique to train an agent to play atari pong game from OpenAI Gym. OpenAI Gym is a toolkit to develop and compare reinforcement learning algorithms. The learning agent takes raw pixels from the atari emulator and predicts an action that is fed back into the emulator via OpenAI interface. The deep reinforcement learning network used in this project is Deep Q Network (DQN), it took over 10 million episodes to perfectly play and win the game.

Pong AI - A Practical Example of Unity ML Agents Usage in Pong Game

RL Agent for Atari Game Pong

Trained a Convolutional Neural Network to use OpenAI gym to play Atari 2600 game Pong using input frames of the game.

A collection of my implemented RL agents for games like Pacman, Pong, SpaceInvaders, Frozenlake, Taxi, Pixelcopter, Pyramids and a lot more by implementing various DRL algorithms using gym, unity-ml, pygame, sb3, rl-zoo and pandagym libraries. To see more advanced & interesting agents, please visit below link:

Use the movements of your thumb to play Pong against a pre-trained Double-DQN-Agent. I used Google-Colab for training the pyTorch model and created the Pong environment with Pygame.

Coordination of two agents being trained by Q-learning (for Pong)

A ping-pong game agent, trained using Genetic Algorithm and Neural Network

For NumPy.org: How to implement a policy gradients deep reinforcement learning agent from scratch and train it to play Pong using screen pixels. Without an autodiff library.

containing two game emulators for multi-agent DQN: Pong and Hunter Prey

This repo implements a DQN RL agent on the roboschool Pong environment using PyTorch.

This work is about creating a DQN agent, able to learn and well perform in the Atari2600 Pong, using Reinfrocement Learning and Deep Q-Network. The code is developed on a jupyter notebook in python while the game environment is provided by OpenAi called "OpenAi Gym".

Deep RL Agent using Proximal Policy Optimization for solving the Pong game.

Implementation of PPO, DQN, VPG agents in Tensorflow to solve Atari Pong and CartPole.

Autonomous agent for the Atari game Pong - Reinforcement Learning :robot:

A multi-agent Gym-compatible environment based off of Atari's Pong.

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Tackling Atari 2600 game Pong with Reinforcement Learning by utilizing DQN and TF-Agents

Deep reinforcement learning project - Training agent to play Atari game Pong in Tensor Flow

A simple Pong game bot trained using Self-Play mechanism.

A project developed as part of exploring deep reinforcement learning applications.

No description available

Pong agent using PyTorch and OpenAI gym

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