My Journey Into Deep Q-Learning with Keras and Gym

主要利用QLearning,DQN,ImprovedDQN(Ddouble DQN) 解决gym框架下的三个问题CartPole-v0,MountainCar-v0,Acrobot-v1

CartPole Gym OpenAI solution using QLearning with Keras

Cart Pole problem solving using RL - QLearning with OpenAI Gym Framework

CartPole-V1 Reinforcement Learning using Qlearning

A QLearning agent written in Python to balance an inverted pendulum

Deep QLearning CartPole using Tensorflow Keras

Implementation of Q-learning on OpenAI's Gym (Gymnasium 0.26), with descriptions

Implementação de Q-Learning para resolver o problema CartPole

This repository implements a Deep Q-Learning algorithm to train an agent to balance a pole on a cart using the CartPole-v1 environment from Gymnasium. The project includes a TensorFlow-based neural network, epsilon-greedy exploration, experience replay, and visualization of the trained agent's performance.

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CartPole_Gym_QLearning

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ML QLearning para resolução do CartPole

This project implements tabular Q-Learning to solve the CartPole-v1 task in Gymnasium. States are discretized into bins and stored in a 4D Q-table.

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A Python library for training an Q-Learning agent to play cartpole.

基于Q-Learning的倒立摆控制

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A solution to the classic CartPole problem from the OpenAI Gym library using Q-learning.

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CartPole-v1 Q-larning realization

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CartPole LQR & Q-Learning control with Isaac Sim 4.5.0 + ROS2, interactive learning webpage

Implementation of Q-learning algorithm for CartPole-v1 environment from OpenAI Gym

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Q Learning agent for Cart Pole