Lunar-Lander-Double-Deep-Q-Networks and aigym_dqn
These are **competitors** — both implement Deep Q-Network variants to solve the same lunar landing problem in OpenAI Gym environments, with Double DQN (A) being a more advanced algorithmic approach than standard DQN (B).
About Lunar-Lander-Double-Deep-Q-Networks
anh-nn01/Lunar-Lander-Double-Deep-Q-Networks
An AI agent that use Double Deep Q-learning to teach itself to land a Lunar Lander on OpenAI universe
This project offers an AI agent that teaches itself how to land a virtual lunar lander safely and quickly on a designated landing pad. By observing its actions and receiving feedback on its performance, the agent learns to control the lander's thrusters. This tool is designed for AI researchers and enthusiasts who are exploring reinforcement learning techniques.
About aigym_dqn
kucharzyk-sebastian/aigym_dqn
Deep Q-Network agent implemented in Python capable of learning to land on the moon in MoonLander-v2 environment from AI Gym library
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