Skip to content

Latest commit

 

History

History
7 lines (4 loc) · 627 Bytes

README.md

File metadata and controls

7 lines (4 loc) · 627 Bytes

Lunar Lander Reinforcement Learning Project

Tyler Kinkade

This was a small exploratory project to compare the effectiveness of artificial agent algorithms ranging in sophistication from naive to deep reinforcement learning (Russell & Norvig, 2022; Sutton & Barto, 2018) to successfully land a virtual "lunar lander" in a Gymnasium (2022) model environment. After comparing the various models, I explored the effects of various hyperparameters on a deep Q-network model (Mnih et al., 2015).

See the project.ipynb Jupyter notebook for the full report.