This repo contains the first project in Udacity's Deep Reinforcement Learning course.
For this project, we will train an agent to navigate and collect bananas in a large, square world. A reward of +1 is provided for collecting a yellow banana, and a reward of -1 is provided for collecting a blue banana. Thus, the goal of this agent is to collect as many yellow bananas as possible while avoiding blue bananas.
The state space has 37 dimensions and contains the agent's velocity, along with ray-based perception of objects around the agent's forward direction. Given this information, the agent has to learn how to best select actions. Four discrete actions are available, corresponding to:
0 - move forward.
1 - move backward.
2 - turn left.
3 - turn right.
The task is episodic, and in order to solve the environment, the agent must get an average score of +13 over 100 consecutive episodes.
Python 3.6 must be installed. The only files required are Navigation_Project.ipynb.
The ipython notebook Navigation_Project.ipynb is the only file required to run this project. The cells must be run in order.