Abstract
We propose a novel training algorithm for reinforcement learning which combines the strength of deep Q-learning with a constrained optimization approach to tighten optimality and encourage faster reward propagation. Our novel technique makes deep reinforcement learning more practical by drastically reducing the training time. We evaluate the performance of our approach on the 49 games of the challenging Arcade Learning Environment, and report significant improvements in both training time and accuracy.
Original language | English (US) |
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State | Published - 2017 |
Event | 5th International Conference on Learning Representations, ICLR 2017 - Toulon, France Duration: Apr 24 2017 → Apr 26 2017 |
Conference
Conference | 5th International Conference on Learning Representations, ICLR 2017 |
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Country/Territory | France |
City | Toulon |
Period | 4/24/17 → 4/26/17 |
ASJC Scopus subject areas
- Education
- Computer Science Applications
- Linguistics and Language
- Language and Linguistics