PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and RL

Iterative Linear Quadratic Regulator with auto-differentiatiable dynamics models

Simple Cartpole example writed with pytorch.

OpenAI's cartpole env solver.

강화학습에 대한 기본적인 알고리즘 구현

Reinforcing Your Learning of Reinforcement Learning

AutoDiff DAG constructor, built on numpy and Cython. A Neural Turing Machine and DeepQ agent run on it. Clean code for educational purpose.

Implementation of Double DQN reinforcement learning for OpenAI Gym environments with PyTorch.

A toolbox for trajectory optimization of dynamical systems

This repository contains CartPole simulator with its GUI, implemented controller (LQR) and generator of random desired position trace. It also contains files to train and test RNN predicting future states of a CartPole.

CartPole control experiments.

This is a pip package implementing Reinforcement Learning algorithms in non-stationary environments supported by the OpenAI Gym toolkit.

Various Control Barrier Functions realized on cartpole.

CartPole game by Reinforcement Learning, a journey from training to inference

NeurIPS 2019: DQN(λ) = Deep Q-Network + λ-returns.

Applying DeepMind's MuZero algorithm to the cart pole environment in gym

Programmatically Interpretable Reinforcement Learning

Run OpenAI Gym on a Server

An implementation of main reinforcement learning algorithms: solo-agent and ensembled versions.

PyTorch implementation of DQN