Orbital

An Astrophysics game embedded with Reinforcement Learning.

Objective:

• Maximize orbit laps

Control:

• Trust toward sun
• Thrust away from sun

Constraints:

• Stay within bounds of game window
• Do not hit the sun
• Do not hit the astroids
• Do not run out of fuel

Under the Hood:

• Game Environment coded from scratch using Pygame
• Q-Learning RL Algorithm coded from scratch using Numpy
• Functions to:
  • Reduce continuous statespace to discrete
  • Handle hyperparameter selection from terminal
  • Train independently in AWS EC2 environments
  • Log relevant learning metrics

Current Project Status:

The agent has trained against itself for 50 million episodes (games) using one set of Q-Learning hyperparameters. Currently, I am refining the algorithm hyperparameters through programatic training, evaluation, and selection.