Popular GitHub Repositories for Lunar

Solving OpenAI Gym problems.

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

Policy Gradient Actor-Critic PyTorch | Lunar Lander v2

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

中国传统黄历 MCP 服务 | Chinese Traditional Almanac MCP Service

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. ??️

Utilizing a pre-trained Mask R-CNN model for the automatic detection of lunar craters

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

Deliverables relating to the Advanced Reinforcement Learning University Unit

Supplementary material for the SPICE-HL3 dataset

Rocket Trajectory Optimization Using REINFORCE Algorithm

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.

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

Solutions for OpenAI Gym RL environments

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

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

Lunar Lander game from OpenAI Gym using behavioral cloning, DAgger methods, and POMDP(Partially-Observable Markov Decision Processes)

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