Project 1: Navigation

Note: The Introduction and Getting Started sections are adapted from the Udacity Deep Reinforcement Learning Nanodegree GitHub repository.

Introduction

For this project, we 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 the 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 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, your agent must get an average score of +13 over 100 consecutive episodes.

Getting Started

Setting up a conda environment

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

1. Create (and activate) a new conda 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. Follow the instructions in this repository to perform a minimal install of OpenAI gym.

   • Next, install the classic control environment group by following the instructions here.
   • Then, install the box2d environment group by following the instructions here.

3. Clone the Udacity Deep Reinforcement Learning Nanodegree GitHub repository, and navigate to the python/ folder. Then, install all dependencies.

   git clone https://github.com/udacity/deep-reinforcement-learning.git
   cd deep-reinforcement-learning/python
   pip install .

4. Create an IPython kernel for the drlnd environment.

   python -m ipykernel install --user --name drlnd --display-name "drlnd"

5. Before running code in a notebook, change the kernel to match the drlnd environment by using the drop-down Kernel menu.

6. 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.

7. Place the file in the in the drlnd-p1-navigation repository folder, and unzip (or decompress) the file.

Exploring the code

• model.py Defines the DQN architecture
• dqn_agent.py Defines the DQN agent

Both these modules closely follow the structure and settings explored in the lessons.

• Navigation_Solution.ipynb Explores and environment, defines a simple training script, and trains the DQN agent. It also includes a cell at the end to visualize the trained agent in action! This is the simplest way to train an agent.