Code for some fun exercises in the textbook 'Reinforcement Learning - An Introduction'

OpenAI Gym cartpole solved by a Neural Network (DQN) in Tensorflow 2

A pole is attached by an un-actuated joint to a cart, which moves along a frictionless track. The system is controlled by applying a force of +1 or -1 to the cart. The pendulum starts upright, and the goal is to prevent it from falling over. A reward of +1 is provided for every timestep that the pole remains upright. The episode ends when the po…

CartPole-CrossEntropyMethod

A reinforcement learning AI agent plays one of our favorite childhood game "Cart Pole"

OpenA.I. challenge for training a system to balance a cart pole.

This project aims to train an artificial neural network to control the cartpole problem using particle swarm optimization.

Hill Climbing Algorithm implemented for the Cart Pole Environment.

Reinforcement learning on OpenAI gym's cartpole environment

CartPole in Java

cartpole environment

Implementing DeepQNetwork and Q learning on gymnasium CartPole-V1 env.

OpenAI CartPole Challange Surpassed | On Windows

a reinforcement learning agent learning to balance the cartpole problem

pytorch implentation of DQN

AWS Reinforcement Learning

Cart-Pole Matlab & ROS/Gazebo Co-simulation framework developed by erc-dynamics.

This repository contains Q-Learning and Deep Q-Learning (DQN) implementations for apprenticeship learning, based on the paper “Apprenticeship Learning via Inverse Reinforcement Learning" by P. Abbeel and A. Y. Ng, applied to two classic control tasks: CartPole and Pendulum.

Developed a Deep Q Network (DQN) for the cartpole balancing problem (a Google gym environment) using screen (pixel) input to allow generalization to other discrete binary problems and expandability into robotics.