This project is my submission for the Navigation project with the Udacity Deep Reinforcement Learning Nanodegree.
In this project we use Deep Q-Learning(DQN) to train an agent to collect bananas within a simple 2d world. The project uses Unity's ML-Agents environment. The goal of the agent is to collect yellow bananas while avoiding blue bananas.
The environment has a state space of 37 dimensions including basic information about the agent and the "ray-based" perception of the objects it can see. Note - For this project, no trainning was undertaken based on pixel values.
The agent receives a reward of +1 for picking up a yellow banana and a -1 penalty/reward for picking up a blue banana. The environment is concsiderd solved when the agent receives an average score of +13 over 100 consecutive episodes.
The agent has four discrete actions to choose from:
- 0 - Forward
- 1 - Backward
- 2 - Left
- 3 - Right
The Unity ML-Agents environment only supports Python 3.6.
Installation instructions for the Unity ML-Agents can be found at Unity ML-Agents and NumPy. Note - You do not require the Unity download for this project.
The following prerequist libraries can be installed as follows:
pip install numpy
pip install torch
pip install unityagents
pip install matplotlib
In a terminal or command window, navigate to the main directory (that contains this README) and run the following command:
$ jupyter notebookThis will open the Jupyter Notebook software and notebook in your browser which you can use to explore and reproduce the experiments that have been run.