/drlnd-p1-navigation

Udacity Deep Reinforcement Learning Nano Degree Project1: Navigation 优大学城深度强化学习纳米学位项目1: 巡航

Primary LanguageJupyter Notebook

Project 1: Navigation

Introduction

For this project, you will train an agent to navigate (and collect bananas!) in a large, square world.

Trained 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 your 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

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

  3. Using conda or other tools such as virtualenv to create a new python 3.6 environment.

  4. In the new python environment install dependencies listed in the requirements.txt file

  5. Clone Unity ML-Agents GitHub repository.

  6. The Unity ML-Agent team frequently releases updated versions of their environment. We are using the v0.4 interface. Please checkout to v0.4 and install the Unity ML-Agent. Installation Instructions

Instructions

In Navigation.ipynb 5 DQN method has been implemented as follows:

  1. Vanilla DQN
  2. Double DQN
  3. Prioritized Experience Replay DQN
  4. Dueling DQN
  5. Mini Rainbow DQN(Double DQN + Prioritized Experience Replay DQN + Dueling DQN)

Note

The project environment is similar to, but not identical to the Banana Collector environment on the Unity ML-Agents GitHub page.