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Interactive Learning of Temporal Features for Control

Code of the paper "Interactive Learning of Temporal Features for Control" published in the IEEE Robotics & Automation Magazine (Special Issue on Deep Learning and Machine Learning in Robotics).

This code is based on the following publication:

  1. Interactive Learning of Temporal Features for Control, preprint availabe here.

Authors: Rodrigo Pérez-Dattari, Carlos Celemin, Giovanni Franzese, Javier Ruiz-del-Solar, Jens Kober.

Link to paper video

This repository includes the code necessary to run the experiments done in simulated environments using human teachers.

Installation

To use the code, it is necessary to first install the gym toolkit (release v0.9.6): https://github.com/openai/gym

Then, the files in the gym folder of this repository should be replaced/added in the installed gym folder in your PC. Two environments were added:

  1. Continuous Mountain Car: the environment outputs an image as an observation.

  2. Inverted Pendulum: the pendulum is bigger and the environments outputs an image as an observation.

Requirements

  • setuptools==38.5.1
  • numpy==1.13.3
  • opencv_python==3.4.0.12
  • matplotlib==2.2.2
  • tensorflow==1.4.0
  • pyglet==1.3.2
  • gym==0.9.6

Usage

  1. To run the main program type in the terminal (inside the folder src):
python main.py --config-file <config_file_name>

To be able to give feedback to the agent, the environment rendering window must be selected/clicked.

Comments

This code has been tested in Ubuntu 18.04 and python >= 3.5.

Troubleshooting

If you run into problems of any kind, don't hesitate to open an issue on this repository. It is quite possible that you have run into some bug we are not aware of.

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Code of the paper "Interactive Learning of Temporal Feature for Control", published in the IEEE Robotics & Automation Magazine.

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