This project repository contains my work for the Udacity's Deep Reinforcement Learning Nanodegree Project 1: Navigation.
For this project, we will train an agent to navigate (and collect bananas!) in a large, square world.The world contains both 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.
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
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 goal of the agent is to collect as many yellow bananas as possible while avoiding blue bananas. The task is episodic, and in order to solve the environment, your agent must get an average score of +13 over 100 consecutive episodes.
To understand this implementation, you will have to have some understanding of Deep Reinforcement Learning. I recommend you reading > Deep Reinforcement learning.
Reinforcement learning refers to goal-oriented algorithms, which learn how to attain a complex objective (goal) or maximize along a particular dimension over many steps; for example, maximize the points won in a game over many moves. They can start from a blank slate, and under the right conditions they achieve superhuman performance. Like a child incentivized by spankings and candy, these algorithms are penalized when they make the wrong decisions and rewarded when they make the right ones – this is reinforcement.
AlphaGo is a famous example of how Deep Reinforcement Learning achieve superhuman performance and defeated the world champion.
This navigation project implements a Value Based method called Deep Q-Networks
The details are taken from the Udacity's Deep Reinforcement Learning Nanodegree program. The environment is based on Unity ML-agents. Please read more about ML-Agents by perusing the GitHub repository.
The project environment is similar to, but not identical to the Banana Collector environment on the Unity ML-Agents GitHub page.
Follow the instructions below to explore the environment on your own machine! You will also learn how to use the Python API to control your agent.
If you haven't already, please 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.
For this project, you will not need to install Unity - this is because we have already built the environment for you, and you can download it 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
Then, place the file in the p1_navigation/ folder in the DRLND GitHub repository, and unzip (or decompress) the file.
(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 "headless" version of the environment. You will not be able to watch the agent without enabling a virtual screen, but you will be able to train the agent. (To watch the agent, you should follow the instructions to enable a virtual screen, and then download the environment for the Linux operating system above.)
After you have followed the instructions above, open Navigation.ipynb (located in the p1_navigation/ folder in the DRLND GitHub repository) and follow the instructions to learn how to use the Python API to control the agent.
For this project, we have built the Unity environment for you, and you must use the environment files that we have provided.
If you are interested in learning to build your own Unity environments after completing the project, you are encouraged to follow the instructions here, which walk you through all of the details of building an environment from a Unity scene.
We have two options for training the agent and they are -
- Directly run the notebook within the online Workspace provided by Udacity Nanodegree for the Project #1 Navigation.
- The online workspace does not allow you to see the simulator of the environment; so, if you want to watch the agent while it is training, you should build your own local environment and make necessary adjustments to the path of the Unity environment and train locally.