README.md

RLND-Navigation-Udacity

This is the navigation project of RL nano degree in Udacity

Requirements

python 3.6
pytorch 0.4.1

Environment

For installation, use the command bellow

pip install unityagents

import the package

from unityagents import UnityEnvironment

Then, download the environment

Next, we will start the environment! Before running the code cell below, change the file_name parameter to match the location of the Unity environment that you downloaded.

Mac: "path/to/Banana.app"
Windows (x86): "path/to/Banana_Windows_x86/Banana.exe"
Windows (x86_64): "path/to/Banana_Windows_x86_64/Banana.exe"
Linux (x86): "path/to/Banana_Linux/Banana.x86"
Linux (x86_64): "path/to/Banana_Linux/Banana.x86_64"
Linux (x86, headless): "path/to/Banana_Linux_NoVis/Banana.x86"
Linux (x86_64, headless): "path/to/Banana_Linux_NoVis/Banana.x86_64"
For instance, if you are using a Mac, then you downloaded Banana.app. If this file is in the same folder as the notebook, then the line below should appear as follows:

env = UnityEnvironment(file_name="Banana.app")

Information of the environment

The state space has 37 dimensions and contains the agent's velocity, along with ray-based perception of objects around agent's forward direction. A reward of +1 is provided for collecting a yellow banana, and a reward of -1 is provided for collecting a blue banana.
The reward in an episode over 15.0 is considerd to be solved.

Code

To train the agent, use the command bellow

python train.py

To do inference, see an trained agent, please use the command bellow

python inference.py

Code and files location

The code of the agent is in agent.py, the pytorch model is in model.py
The plot of trainig scores is in scores.png
Report of algorithm is in Report of P1 Navigation.pdf