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Gradient-based minimization for multi-expert Inverse Reinforcement Learning

Tateo, Davide LU orcid ; Pirotta, Matteo ; Restelli, Marcello and Bonarini, Andrea (2017) 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings 2018-January. p.1-8
Abstract

We present a model-free method for solving the Inverse Reinforcement Learning (IRL) problem given a set of trajectories generated by different experts' policies. In many applications, the observed demonstrations are not produced by the same policy. In fact, they may be provided by multiple experts that follow different (but similar) policies or even by the same expert that does not always replicate the same policy (e.g., human expert). We propose to model different experts' demonstrations as generated by policies sampled from a distribution. Differently from many other IRL approaches, the proposed methodology requires neither knowledge about the dynamics of the system nor to iteratively solve the direct problem for different candidate... (More)

We present a model-free method for solving the Inverse Reinforcement Learning (IRL) problem given a set of trajectories generated by different experts' policies. In many applications, the observed demonstrations are not produced by the same policy. In fact, they may be provided by multiple experts that follow different (but similar) policies or even by the same expert that does not always replicate the same policy (e.g., human expert). We propose to model different experts' demonstrations as generated by policies sampled from a distribution. Differently from many other IRL approaches, the proposed methodology requires neither knowledge about the dynamics of the system nor to iteratively solve the direct problem for different candidate reward functions, thus providing an efficient solution to the IRL problem.

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author
; ; and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
series title
2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
volume
2018-January
pages
8 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
conference location
Honolulu, United States
conference dates
2017-11-27 - 2017-12-01
external identifiers
  • scopus:85046080056
ISBN
9781538627259
DOI
10.1109/SSCI.2017.8280919
language
English
LU publication?
no
id
8ee9154b-47d1-4e8c-b667-906e816be220
date added to LUP
2025-10-16 14:43:38
date last changed
2025-10-24 03:39:46
@inproceedings{8ee9154b-47d1-4e8c-b667-906e816be220,
  abstract     = {{<p>We present a model-free method for solving the Inverse Reinforcement Learning (IRL) problem given a set of trajectories generated by different experts' policies. In many applications, the observed demonstrations are not produced by the same policy. In fact, they may be provided by multiple experts that follow different (but similar) policies or even by the same expert that does not always replicate the same policy (e.g., human expert). We propose to model different experts' demonstrations as generated by policies sampled from a distribution. Differently from many other IRL approaches, the proposed methodology requires neither knowledge about the dynamics of the system nor to iteratively solve the direct problem for different candidate reward functions, thus providing an efficient solution to the IRL problem.</p>}},
  author       = {{Tateo, Davide and Pirotta, Matteo and Restelli, Marcello and Bonarini, Andrea}},
  booktitle    = {{2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings}},
  isbn         = {{9781538627259}},
  language     = {{eng}},
  month        = {{07}},
  pages        = {{1--8}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings}},
  title        = {{Gradient-based minimization for multi-expert Inverse Reinforcement Learning}},
  url          = {{http://dx.doi.org/10.1109/SSCI.2017.8280919}},
  doi          = {{10.1109/SSCI.2017.8280919}},
  volume       = {{2018-January}},
  year         = {{2017}},
}