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Introduction

Trained Agent

This is an excercise to train an RL agent using DQN to navigate (and collect bananas!) in a large, square world. This is a customised version of Banana Collecter from Unity ML agents.

The agent

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 the agent's forward direction. Given this information, the agent has to learn how to best select actions.

State and Action Space

Four discrete actions are available, corresponding to:

  • 0 - walk forward
  • 1 - walk backward
  • 2 - turn left
  • 3 - turn right

This is an episodic task, in order to solve the environment, the agent must get an average score of +13 over 100 consecutive episodes.

Running the code:

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

    (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. Install dependencies from requirements.txt file using:

pip3 install -r requirements.txt

  1. Place the file inside the main folder of this repository, and unzip (or decompress) the file.

  2. Run Navigation.ipynb to train the agent then use the provided function to watch trained agent navigating the Banana environment!

License

DQN_Navigator is released under the MIT license. See LICENSE for more information.