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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

Deep Q-learning From Demonstrations

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Todd Hester

Google DeepMind


Matej Vecerik

Google DeepMind


Olivier Pietquin

Google DeepMind


Marc Lanctot

Google DeepMind


Tom Schaul

Google DeepMind


Bilal Piot

Google DeepMind


Dan Horgan

Google DeepMind


John Quan

Google DeepMind


Andrew Sendonaris

Google DeepMind


Ian Osband

Google DeepMind


Gabriel Dulac-Arnold

Google DeepMind


John Agapiou

Google DeepMind


Joel Leibo

Google DeepMind


Audrunas Gruslys

Google DeepMind


DOI:

10.1609/aaai.v32i1.11757


Abstract:

Deep reinforcement learning (RL) has achieved several high profile successes in difficult decision-making problems. However, these algorithms typically require a huge amount of data before they reach reasonable performance. In fact, their performance during learning can be extremely poor. This may be acceptable for a simulator, but it severely limits the applicability of deep RL to many real-world tasks, where the agent must learn in the real environment. In this paper we study a setting where the agent may access data from previous control of the system. We present an algorithm, Deep Q-learning from Demonstrations (DQfD), that leverages small sets of demonstration data to massively accelerate the learning process even from relatively small amounts of demonstration data and is able to automatically assess the necessary ratio of demonstration data while learning thanks to a prioritized replay mechanism. DQfD works by combining temporal difference updates with supervised classification of the demonstrator’s actions. We show that DQfD has better initial performance than Prioritized Dueling Double Deep Q-Networks (PDD DQN) as it starts with better scores on the first million steps on 41 of 42 games and on average it takes PDD DQN 83 million steps to catch up to DQfD’s performance. DQfD learns to out-perform the best demonstration given in 14 of 42 games. In addition, DQfD leverages human demonstrations to achieve state-of-the-art results for 11 games. Finally, we show that DQfD performs better than three related algorithms for incorporating demonstration data into DQN.

Topics: AAAI

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HOW TO CITE:

Todd Hester||Matej Vecerik||Olivier Pietquin||Marc Lanctot||Tom Schaul||Bilal Piot||Dan Horgan||John Quan||Andrew Sendonaris||Ian Osband||Gabriel Dulac-Arnold||John Agapiou||Joel Leibo||Audrunas Gruslys Deep Q-learning From Demonstrations Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Todd Hester||Matej Vecerik||Olivier Pietquin||Marc Lanctot||Tom Schaul||Bilal Piot||Dan Horgan||John Quan||Andrew Sendonaris||Ian Osband||Gabriel Dulac-Arnold||John Agapiou||Joel Leibo||Audrunas Gruslys Deep Q-learning From Demonstrations AAAI 2018, .

Todd Hester||Matej Vecerik||Olivier Pietquin||Marc Lanctot||Tom Schaul||Bilal Piot||Dan Horgan||John Quan||Andrew Sendonaris||Ian Osband||Gabriel Dulac-Arnold||John Agapiou||Joel Leibo||Audrunas Gruslys (2018). Deep Q-learning From Demonstrations. Proceedings of the AAAI Conference on Artificial Intelligence, 32, .

Todd Hester||Matej Vecerik||Olivier Pietquin||Marc Lanctot||Tom Schaul||Bilal Piot||Dan Horgan||John Quan||Andrew Sendonaris||Ian Osband||Gabriel Dulac-Arnold||John Agapiou||Joel Leibo||Audrunas Gruslys. Deep Q-learning From Demonstrations. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Todd Hester||Matej Vecerik||Olivier Pietquin||Marc Lanctot||Tom Schaul||Bilal Piot||Dan Horgan||John Quan||Andrew Sendonaris||Ian Osband||Gabriel Dulac-Arnold||John Agapiou||Joel Leibo||Audrunas Gruslys. 2018. Deep Q-learning From Demonstrations. "Proceedings of the AAAI Conference on Artificial Intelligence, 32". .

Todd Hester||Matej Vecerik||Olivier Pietquin||Marc Lanctot||Tom Schaul||Bilal Piot||Dan Horgan||John Quan||Andrew Sendonaris||Ian Osband||Gabriel Dulac-Arnold||John Agapiou||Joel Leibo||Audrunas Gruslys. (2018) "Deep Q-learning From Demonstrations", Proceedings of the AAAI Conference on Artificial Intelligence, 32, p.

Todd Hester||Matej Vecerik||Olivier Pietquin||Marc Lanctot||Tom Schaul||Bilal Piot||Dan Horgan||John Quan||Andrew Sendonaris||Ian Osband||Gabriel Dulac-Arnold||John Agapiou||Joel Leibo||Audrunas Gruslys, "Deep Q-learning From Demonstrations", AAAI, p., 2018.

Todd Hester||Matej Vecerik||Olivier Pietquin||Marc Lanctot||Tom Schaul||Bilal Piot||Dan Horgan||John Quan||Andrew Sendonaris||Ian Osband||Gabriel Dulac-Arnold||John Agapiou||Joel Leibo||Audrunas Gruslys. "Deep Q-learning From Demonstrations". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Todd Hester||Matej Vecerik||Olivier Pietquin||Marc Lanctot||Tom Schaul||Bilal Piot||Dan Horgan||John Quan||Andrew Sendonaris||Ian Osband||Gabriel Dulac-Arnold||John Agapiou||Joel Leibo||Audrunas Gruslys. "Deep Q-learning From Demonstrations". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Todd Hester||Matej Vecerik||Olivier Pietquin||Marc Lanctot||Tom Schaul||Bilal Piot||Dan Horgan||John Quan||Andrew Sendonaris||Ian Osband||Gabriel Dulac-Arnold||John Agapiou||Joel Leibo||Audrunas Gruslys. Deep Q-learning From Demonstrations. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
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