Project 1: Navigation

Introduction

For this project, a DQN and Double DQN agents were implemented and trained to navigate (and collect bananas!) in a large, square world. This project is part of the Deep Reinforcement Learning Nanodegree program.

Rewards

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 DQN and Double DQN agents is to collect as many yellow bananas as possible while avoiding blue bananas.

Environment

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

Download DRLND repository

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

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

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/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.

Download Unity Environment

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 "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.)

Place the file in the DRLND GitHub repository, in the p1_navigation/ folder, and unzip (or decompress) the file.

Instructions

Follow the instructions in DQN/ and DoubleDQN/ folders to get started with training the agents agent!

Implementation Details

All implementation details and results are found in Report/ folder

References

• [1]: V. Mnih et al., "Human-level control through deep reinforcement learning", Nature, vol. 518, no. 7540, pp. 529-533, 2015. Available: 10.1038/nature14236 [Accessed 3 September 2021].
• [2]: U. Technologies, "Machine Learning Agents | Unity", Unity, 2021. [Online]. Available: https://unity.com/products/machine-learning-agents. [Accessed: 03- Sep- 2021].
• [3]: R. Sutton and A. Barto, Reinforcement Learning, 2nd ed. 2019.
• [4]: H. van Hasselt, A. Guez and D. Silver, "Deep Reinforcement Learning with Double Q-learning", 2015. [Accessed 3 September 2021].
• [5]: M. Hessel et al., "Rainbow: Combining Improvements in Deep Reinforcement Learning", 2021. [Accessed 3 September 2021].