cart-pole

University group project concerning the implementation of an iterative learning controller tested on a Cart-Pole system. The code has been implemented in Matlab.

Disentangling Sources of Uncertainty for Active Exploration (Reinforcement Learning)

C# source code for simulation of the cart and pole balancing task.

Comparative study: Quantum vs. classical models for Cart Pole. Examining entanglement layers and data re-uploading, highlighting quantum model superiority.

first attempts on deep RL techniques

A simple DQN implementation in Unity3D.

Cart pole balancing

My implementation of LQR and MPC control for the classic inverted pendulum controls problem.

Cross-language numerical simulation collection implementing a 2D heat-equation FTCS solver and an inverted-pendulum cart–pole with RK4 and sliding-mode control in C#, C++, Java, Python, Julia, Go, Scala, Ruby, and Rust, with plots, CSV logs, frames, and optional FFmpeg video.

Using Genetic programming for sloving balancing double pendulum problem. The works demonstrates the difference of control performance by using non-Markov and Markov decision process

Modern applied deep learning with reinforcement methodology.

This project serves as an introduction to Deep Q-Learning and reinforcement learning concepts. The trained agent learns to balance the cart-pole system through iterative training and evaluation. You can modify the environment or parameters to further experiment with different reinforcement learning strategies.

This repo implements the REINFORCE algorithm for solving the Cart Pole V1 environment of the Gymnasium library using Python 3.8 and PyTorch 2.0.1.

MATLAB implementation of a Safety-Aware Reward Shaping (SARS) framework for data-driven, model-free control of an inverted pendulum using DDPG and PPO. Submitted to IEEE PIICON 2026.

Sim-to-real RL for all 56 equilibrium transitions of a triple inverted pendulum on a cart. MuJoCo + TQC + n8n-orchestrated pipeline.

A real-time simulator of the cart-pole control system.

Collection of examples for Reinforcement Learning

Q Learning model for a cart-pole problem