Project Description

In this project we train an agent to walk around an environment and collect bananas.

You get +1 for each yellow you collect and -1 for each blue one. The goal is to get a score of >=13 over 100 consecutive episodes (note: we train to 15 because we're bananas for this project)

Copied from project description: The state space has 37 dimensions and contains the agent's velocity, along with ray-based perception of objects around 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.

Getting Started

To run the code a unity environment is needed that can be downloaded as desribed below:

1. Download the environment from one of the links below. You need only select the environment that matches your operating system:

   • Linux: click here
   • Mac OSX: click here
   • Windows (32-bit): click here
   • Windows (64-bit): click here

   (For Windows users) Check out this link if you need help with determining if your computer is running a 32-bit version or 64-bit version of the Windows operating system.

   (For AWS) If you'd like to train the agent on AWS (and have not enabled a virtual screen), then please use this link to obtain the environment.

2. Update the line in Navigation.ipynb that starts with env = UnityEnvironment( to wherever you placed the env.

3. Dependencies: pip install each of the following:

   • Pillow>=4.2.1
   • matplotlib
   • numpy>=1.11.0
   • jupyter
   • pytest>=3.2.2
   • docopt
   • pyyaml
   • protobuf==3.5.2
   • grpcio==1.11.0
   • torch==0.4.0
   • pandas
   • scipy
   • ipykernel
   • unityagents==0.4.0

Instructions

Follow the instructions in Navigation.ipynb to get started with training your own agent!

Report

There is also a writeup of the algorithm used in Report.md