32 projects in the framework of Deep Reinforcement Learning algorithms: Q-learning, DQN, PPO, DDPG, TD3, SAC, A2C and others. Each project is provided with a detailed training log.

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

Python implementation of MPPI (Model Predictive Path-Integral) controller to understand the basic idea. Mandatory dependencies are numpy and matplotlib only.

Deep Reinforcement Learning in C#

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.

Implementation and examples from Trajectory Optimization with Optimization-Based Dynamics https://arxiv.org/abs/2109.04928

A toolbox for trajectory optimization of dynamical systems

『PythonとCasADiで学ぶモデル予測制御』 サポートサイト

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

CartPole control experiments.

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

使用pytorch构建深度强化学习模型DQN

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

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.