tiagovcosta / DRLND_P1_Navigation

DRLND Project 1: Navigation

In this project a RL agent was trained to navigate and collect bananas in a large, square world, using the Deep Q-Network algorithm.

Algorithm and implementation details are available in Report.md.

This project is part of the Udacity Deep Reinforcement Learning Nanodegree.

Environment

The environment consists of a square world containing yellow and blue bananas.

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, the agent must get an average score of +13 over 100 consecutive episodes.

Getting Started

1. Clone and setup the DRLND Repository

   Follow the instructions in the DRLND GitHub repository to set up your Python environment. These instructions can be found in README.md at the root of the repository. By following these instructions, you will install PyTorch, the ML-Agents toolkit, and a few more Python packages required to complete the project.

   (For Windows users) The ML-Agents toolkit supports Windows 10. While it might be possible to run the ML-Agents toolkit using other versions of Windows, it has not been tested on other versions. Furthermore, the ML-Agents toolkit has not been tested on a Windows VM such as Bootcamp or Parallels.

2. Clone this repository

   git clone https://github.com/tiagovcosta/DRLND_P1_Navigation.git
   

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 your local clone of this repository, and unzip (or decompress) the file.

Instructions

1. Open Navigation.ipynb

   cd DRLND_P1_Navigation/
   conda activate drlnd
   jupyter notebook
   

2. Follow the instructions in Navigation.ipynb to either train a new agent or watch a trained agent.

   Pre-trained agent model weights are available in agent_weights.pth.