Popular GitHub Repositories for Lunar Lander

Solving OpenAI Gym problems.

Twin Delayed DDPG (TD3) PyTorch solution for Roboschool and Box2d environment

Deep Reinforcement Learning by using Proximal Policy Optimization and Random Network Distillation in Tensorflow 2 and Pytorch with some explanation

Open source code combining implementations of Upside Down Reinforcement Learning and Reward Conditioned Policies

An AI agent that use Double Deep Q-learning to teach itself to land a Lunar Lander on OpenAI universe

Collection of my Reinforcement Learning (RL) practices including DQN, D3QN, and Adaptive Gamma, applied to the Lunar Lander and CartPole environments. ??️

An implementation of Double Deep Q-Network (DDQN) to solve the game Lunar Lander.

My DRL(Deep Reinforcement Learning ) algorithm demo, base on pytorch and gym environment.

Deliverables relating to the Advanced Reinforcement Learning University Unit

Rocket Trajectory Optimization Using REINFORCE Algorithm

This is a Deep Reinforcement Learning solution for the Lunar Lander problem in OpenAI Gym using dueling network architecture and the double DQN algorithm.

Code and Report relating to the Advanced Reinforcement Learning University Unit

Lunar Lander Environment Analysis and Modifications [Introduction to Intelligent and Autonomous Systems Course Project]

Deep Q-Learning consists of combining Q-Learning with Artificial Neural Networks. Inputs are encoded vectors, each one defining a state of the environment. These inputs go to an Artificial Neural Network, where the output is the action to play

Using a deep Q-learning network and searching for optimal hyperparameters in order to solve the lunar lander problem provided by OpenAI Gym.

Implement DQN agent on OpenAI's environment LunarLander-v2

Training an agent to land a spacecraft in the LunarLander environment.

DQN Solution for LunarLander-v3 (Gymnasium) | Reinforcement learning agent using Deep Q-Network (DQN) to land a lunar module safely. Implements experience replay, target networks, and training visualization. This project serves as a hands-on reinforcement learning experiment for solving continuous control tasks using deep Q-learning.