Super-mario-bros-PPO-pytorch and ppo-rocket-landing
About Super-mario-bros-PPO-pytorch
vietnh1009/Super-mario-bros-PPO-pytorch
Proximal Policy Optimization (PPO) algorithm for Super Mario Bros
This project helps reinforcement learning researchers and AI enthusiasts train an agent to master the classic video game Super Mario Bros. By applying the Proximal Policy Optimization (PPO) algorithm, you can input the game environment and output a highly capable AI agent that can complete almost all levels of the game. It is designed for those looking to explore advanced AI training for game-playing.
About ppo-rocket-landing
taherfattahi/ppo-rocket-landing
Proximal Policy Optimization (PPO) algorithm using PyTorch to train an agent for a rocket landing task in a custom environment
This project helps aerospace engineers and researchers develop and test AI agents for controlling rocket landings or hovering. It takes in simulated rocket physics data, including position, velocity, and angles, and outputs optimal thrust and nozzle adjustments. The end-user is typically someone working on spacecraft guidance, autonomous systems, or aerospace simulation.
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