Abstract
In this work we present a technique for using natural language to help reinforcement learning generalize to unseen environments using neural machine translation techniques. These techniques are then integrated into policy shaping to make it more effective at learning in unseen environments. We evaluate this technique using the popular arcade game, Frogger, and show that our modified policy shaping algorithm improves over a Q-learning agent as well as a baseline version of policy shaping.
Original language | English |
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Title of host publication | 17th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018 |
Pages | 1956-1958 |
Number of pages | 3 |
State | Published - 2018 |
Event | 17th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018 - Stockholm, Sweden Duration: Jul 10 2018 → Jul 15 2018 |
Publication series
Name | Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS |
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Volume | 3 |
ISSN (Print) | 1548-8403 |
ISSN (Electronic) | 1558-2914 |
Conference
Conference | 17th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018 |
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Country/Territory | Sweden |
City | Stockholm |
Period | 7/10/18 → 7/15/18 |
Bibliographical note
Publisher Copyright:© 2018 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
Keywords
- Interactive machine learning
- Policy shaping
- Reinforcement learning
ASJC Scopus subject areas
- Artificial Intelligence
- Software
- Control and Systems Engineering