The state is an 8-dimensional vector: the coordinates of the lander in x & y, its linear velocities in x & y, its angle, its angular velocity, and two booleans that represent whether each leg is in contact with the ground or not.

This repository contains my successful solution to the Lunar Lander environment from OpenAI Gym using Deep Q-Learning. Check out the interactive notebook, trained model, and impressive landing video showcasing the AI agent's mastery of lunar landings.

Here is an implementation of a reinforcement learning agent that solves the OpenAI Gym’s Lunar Lander environment. This environment consists of a lander that, by learning how to control 4 different actions, has to land safely on a landing pad with both legs touching the ground.

Nov 10, 2023 · The Lunar Lander from OpenAI gym is part of the Box2D environments and represents a rocket trajectory optimization problem. The environment uses the Pontryagin’s maximum principle, whereby...

Solving OpenAI Lunar Lander Box2D game using reinforcement learning. The task accomplished by this project is to build an AI agent for the game of Lunar Lander defined by openAI gym in Box2D format. Here, a lunar lander needs to land with zero velocity between the flags on a landing pad as shown in the figure. with a constant high reward.

The goal was to solve the Lunar Lander (v2) environment provided with the OpenAI Gym. I was very excited about the semi-recent advancement of DeepMind's Deep Q-Networks, and so I did a custom implementation built only using the DQN paper "Human-level control through deep reinforcement learning."

Apr 23, 2020 · I am working on solving OpenAI Gym's Continuous Lunar Lander - v2 environment using DDPG. The observation space consists of 8 values. But I couldn't understand what are those 8 values. Can someone explain what do they exactly stand for?

We will use OpenAI Gym, which is a popular toolkit for reinforcement learning (RL) algorithms. Concretely, we are going to take the Lunar Lander environment, define a search space and describe it as an optimization problem, and use Trieste to find an optimal solution for the problem.

Apr 26, 2023 · The Lunar Lander is a classic reinforcement learning environment provided by OpenAI’s Gym library. The goal is to develop an intelligent agent capable of landing a lunar module safely on...

Oct 30, 2023 · We tried to provide well-annotated code snippets, with an insightful discussion on artificial intelligence followed by the Lunar Lander problem, encouraging a deeper comprehension of...