cartpole
There are 143 repositories under cartpole topic.
arminsadreddin/CartPole-DQN
This is a solution for CartPole game using deep Q learning and Openai.gym library
babyapple/tidy-rl
Tensorflow implementation of reinforcement learning (PG, A2C, DQN, DDPG, PPO, HER, SAC)
ioarun/openai-gym
:space_invader: My solutions to OpenAI Gym Reinforcement Learning problems.
Yangyangii/Curiosity-Driven-A2C
Implementation of Curiosity-Driven Exploration with PyTorch
sorryformyself/tensorflow2_cartpole
（deep reinforcement learning) An tensorflow2 implementation of DQN and improvements. Solved after 31s using cpu.
adesgautam/Reinforcement-Learning
Reinforcement learning algorithms to solve OpenAI gym environments
xffxff/spinningup-cpp
Implement some of the core deep RL algorithms with C++
farahbakhsh3/PSO_CartPole
Using PSO algorithm to play CartPole
MagicCube/cart-pole-js
The classic Cart Pole game implemented in JavaScript, and powered by TensorFlow.js.
miaoz0/DQN_CartPole_tf
强化学习 CartPole环境，Tensorflow实现DQN。
nikuleo/DRL-Pytorch-DEMO
My DRL(Deep Reinforcement Learning ) algorithm demo, base on pytorch and gym environment.
andri27-ts/ClassicCartPole
CartPole using Policy Gradient Model Based
arcuma/ocpy
Optimal control solver implemented in Python. SymPy for symbolic differentiation and Numba for fast computation.
kevinniechen/qlearn-cartpole
Q-Learning for Cartpole (CMSC389F)
nikhilpodila/Reinforcement-Learning-Inverted-Pendulum
This is the repository of the Final Semester Undergraduation Project on Reinforcement Learning (Inverted Pendulum problem) done by Nikhil Podila and Savinay Nagendra. The project was performed under the guidance of Professor Koshy George at the Center of Intelligent Systems in PES Institute of Technology, Bangalore, India
sritee/Stochastic-Policy-Gradient-Methods
Monte-Carlo Policy Gradient, Stochastic Policy Gradient and Numerical Gradient Policy Gradient
TTitcombe/CartPoleSwingUp
Custom environment for OpenAI gym
111989/cartpole_v1
Balancing CartPole-v1 from OpenAI Gym by employing Epsilon-Greedy strategy for Q-Learning, and by means of Genetic Algorithm.
a13xe/PolicyGradientAlgorithms
Comparing VPG, TRPO and PPO from Policy Gradient family
Arancew/simple_forcement_Learning
基于pytoch的俩个简单的强化学习案例
DrLux/DDPG_on_DMControl_Suite
Solving ceetah,cartpole,reacher,walker Deepmind Control Suite using DDPG (Pythorc)
erc-dynamics/cart_pole
Cart-Pole Matlab & ROS/Gazebo Co-simulation framework developed by erc-dynamics.
fschur/Evolutionary-Reinforcement-Learning-for-OpenAI-Gym
Implementation of Augmented-Random-Search for OpenAI Gym environments
gunh0/reinforcement-learning-cartpole-balancing
📢 2019 Microsoft Student Partners (MSP) Evangelism Seminar - 2019.03.31
michaelw123/gym-scala-client
A scala client for openai gym
molomono/CartPole_Optimized_DDQN
This repository contains the implementation of a DDQN agent to solve the CartPole-v0 problem. I use openai gym to simulate the environment.
philipshurpik/reinforce-experiments
Simple implementations of vanilla reinforce (policy gradient) and actor critic methods with numpy and different frameworks
SC-SGS/High-Fidelity-Cartpole
High-fidelity cartpole environment for reinforcement learning
sebastienbaur/Deep-RL-OpenAI-gym
Deep RL on OpenAI gym environment
TTitcombe/DQN
PyTorch implementation of Deep Q Learning
AliBakly/CartPole-A2C-reinforcement-learning
This repository contains the implementation of the K-workers, n-step Advantage Actor-Critic (A2C) algorithm applied to the CartPole environment, as part of a reinforcement learning project for the EPFL Spring Semester 2024 course on Artificial Neural Networks and Reinforcement Learning.
benborder/drla-sim
Trains a deep reinforcement learning agent in simulation testbed environments with the DRLA library.
ojasraundale/cartpole
Implementation of Black Box Optimization methods using Fourier State Vectors on the Cartpole Domain.
Shaz-5/apprenticeship-IRL
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.