A novel update mechanism for Q-Networks based on extreme learning machines

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Abstract

Reinforcement learning is a popular machine learning paradigm which can find near optimal solutions to complex problems. Most often, these procedures involve function approximation using neural networks with gradient based updates to optimise weights for the problem being considered. While this common approach generally works well, there are other update mechanisms which are largely unexplored in reinforcement learning. One such mechanism is Extreme Learning Machines. These were initially proposed to drastically improve the training speed of neural networks and have since seen many applications. Here we attempt to apply extreme learning machines to a reinforcement learning problem in the same manner as gradient based updates. This new algorithm is called Extreme Q-Learning Machine (EQLM). We compare its performance to a typical Q-Network on the cart-pole task - a benchmark reinforcement learning problem - and show EQLM has similar long-term learning performance to a Q-Network.
Original languageEnglish
Number of pages7
Publication statusPublished - 24 Jul 2020
EventIEEE World Congress on Computational Intelligence 2020 - Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020
https://wcci2020.org/

Conference

ConferenceIEEE World Congress on Computational Intelligence 2020
Abbreviated titleWCCI
CountryUnited Kingdom
CityGlasgow
Period19/07/2024/07/20
Internet address

Keywords

  • reinforcement learning
  • extreme learning machine (ELM)
  • neural network

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  • Activities

    • 1 Oral presentation

    A novel update mechanism for Q-Networks based on extreme learning machines

    Callum Wilson (Speaker)
    23 Jul 2020

    Activity: Talk or presentation typesOral presentation

    Cite this

    Wilson, C., Riccardi, A., & Minisci, E. (2020). A novel update mechanism for Q-Networks based on extreme learning machines. Paper presented at IEEE World Congress on Computational Intelligence 2020, Glasgow, United Kingdom.