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Autonomous Exploration Agent

Obstacle-ridden environment exploration with Unity3D and ML-Agents

Overview

This work implements a searching agent capable of exploring cluttered environments using Reinforcement Learning. The agent moves in a randomly generated space filled with obstacles and is incentivated to avoid obstacles in its path while searching for the target.

Live demo

Paper (attended WOA2020)

Presentation

Prerequisites

Structure

The repository is structured as follows:

  • src contains:
    • config which holds the hyperparameter configurations for PPO and the curricula for Curriculum Learning
    • notebooks which holds notebooks for Jupyter for testing and extracting data from the mlagents Python package (which contains the actual RL implementations)
    • UnitySDK holds Unity's source code for the environment, consisting of scenes, agent and other classes, etc.
  • docs contains a formalized report of the project and a presentation.
  • webapp contains a demo of the project.

Installation

$ git clone https://github.com/dodicin/autonomous-exploration-agent
$ cd autonomous-exploration-agent
$ virtualenv .venv -p C:/Python/Python36/python.exe 
$ .venv/Script/activate.bat
$ pip install mlagents

If pip gives some errors for the bleach and pillow packages:

$ pip uninstall pillow==6.1.0 // Uninstall the uncompatible version
$ pip install pillow==5.4.1

$ pip uninstall bleach==3.1.0
$ pip install bleach==1.5.0

Training

  • Open the project
  • Tick the Control checkboxes in the Broadcast Hub for the Brains that are going to be trained
  • Remove the model from the Brain objects under ./Assets/Brains
  • Launch a command prompt:
$ tensorboard --logdir=summaries
$ mlagents-learn src\config\trainer_config.yaml --curriculum=src\config\curricula\autonomous-exploration-agent\ --run-id=<RunID> --train
  • Stop the training when the cumulative reward reaches a satisfying level
  • Models are saved under ./models

Testing

  • Open the project
  • Unick the Control checkboxes in the Broadcast
  • Add the models to the Brain objects under ./Assets/Brains. The models are found under ./models
  • Press the play button to see the trained model in action

Authors