Crossmodal attentive skill learner
Name
1711.10314.pdf
Description
Accepted version
Size
2.55 MB
Format
Adobe PDF
Checksum (MD5)
4cbad2ad690c6126106de2fc3c8fb37e
Author(s)
Omidshafiei, Shayegan
Kim, Dong-Ki
Pazis, Jason
How, Jonathan P.
Date Issued
2018
Citation
Omidshafiei, Shayegan, Kim, Dong-Ki, Pazis, Jason and How, Jonathan P. 2018. "Crossmodal attentive skill learner."
Version
Author's final manuscript
Abstract
© 2018 International Foundation for Autonomous Agents and Multiagent Systems. This paper introduces the Crossmodal Attentive Skill Learner (CASL), Integrated with the recently-introduced Asynchronous Advantage Option-Critic (A2OC) architecture (15] to enable hierarchical rei nforcement learning across multiple sensory inputs. We provide concrete examples where the approach not only improves perform ance in a single task, but accelerates transfer to new tasks. We demonstrate the attention mechanism anticipates and identifies useful latent features, while filtering irrelevant sensor modalities during execution. We modify the Arcade Learning Environment (7] to support audio queries, and conduct evaluations of crossmodal learning in the Atari 2600 games H.E.R.O. and Amidar. Finally, buildi ng on the recent work of Babaeizadeh et aL [4], we open-soulce a fast hybrid CPU-CPU implementation of CASL.
MIT Department
Massachusetts Institute of Technology. Aerospace Controls Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
http://ifaamas.org/Proceedings/aamas2018/pdfs/p139.pdf
