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Subgoal Discovery and Language Learning in Reinforcement Learning Agents| old_uid | 14399 |
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| title | Subgoal Discovery and Language Learning in Reinforcement Learning Agents |
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| start_date | 2014/09/30 |
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| schedule | 14h |
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| online | no |
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| location_info | salle 25-26 |
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| summary | As intelligent agents and robots become more commonly used, methods to make interaction with the agents more accessible will become increasingly important. In this talk, I will present a system for intelligent agents to learn task descriptions from linguistically annotated demonstrations, using a reinforcement learning framework based on object-oriented Markov decision processes (OO-MDPs). Our framework learns how to ground natural language commands into reward functions, using as input demonstrations of different tasks being carried out in the environment. Because language is grounded to reward functions, rather than being directly tied to the actions that the agent can perform, commands can be high-level and can be carried out autonomously in novel environments. Our approach has been empirically validated in a simulated environment with both expert-created natural language commands and commands gathered from a user study.
I will also describe a related, ongoing project to develop novel option discovery methods for OO-MDP domains. These methods permit agents to identify new subgoals in complex environments that can be transferred to new tasks. We have developed a framework called Portable Multi-policy Option Discovery for Automated Learning (P-MODAL), an approach that extends the PolicyBlocks option discovery approach to OO-MDPs.
This work is collaborative research with Dr. Michael Littman and Dr. James MacGlashan of Brown University, Dr. Smaranda Muresan of Columbia University. A number of UMBC students have contributed to the project: Shawn Squire, Nicholay Topin, Nick Haltemeyer, Tenji Tembo, Michael Bishoff, Rose Carignan, and Nathaniel Lam. |
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| responsibles | El Fallah-Seghrouchni, Maudet, Moraitis |
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