In this project, I will demonstrate a Deep Q Learning approach to train an agent to solve the provided Banana navigation environment.

The trained agent to navigates (and collects bananas!) in a large, square world.

• Functional, well-documented, and organized code for training the agent implemented in PyTorch and Python 3
• Saved model weights for my successful agent

The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source Unity plugin that enables games and simulations to serve as environments for training intelligent agents. Agents can be trained using reinforcement learning, imitation learning, neuroevolution, or other machine learning methods through a simple-to-use Python API.

In this task, I trained 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 my agent was 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 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, my agent had to get an average score of +13 over 100 consecutive episodes.

To set up your python environment to run the code in this repository, follow the instructions below.

1. Create (and activate) a new environment with Python 3.6.

   • Linux or Mac:

   conda create --name drlnd python=3.6
   source activate drlnd

   • Windows:

   conda create --name drlnd python=3.6 
   activate drlnd

2. Clone the repository (if you haven't already!), and navigate to the python/ folder. Then, install several dependencies.

   git clone https://github.com/bohoro/Navigation.git
   cd Navigation/python
   pip install .

3. 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.

4. Place the file in the DRLND GitHub repository, in the Navigation/ folder, and unzip (or decompress) the file.

1. Run the pre-trained agent. This step will load the saved weights and run 3 episodes of the task.

   cd Navigation/
   python run_agent.py

2. Train and agent from scratch

   python UnityAgentDriver.py

For more information on the underlying technology see the Report.