Reinforcement Learning for Quadcopter Control

Trained a Deep Q-Network to control a quadcopter to autonomously avoid obstacles.

A low-level quadcopter controller from a library handled stabilization, while motion primitives discretized the action space so the policy could be learned more easily and run as a real-time controller.