Learning to Interactively Learn and Assist
Autor: | Karol Hausman, Mark Woodward, Chelsea Finn |
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Jazyk: | angličtina |
Rok vydání: | 2019 |
Předmět: |
FOS: Computer and information sciences
Vocabulary Computer Science - Machine Learning Computer science Computer Science - Artificial Intelligence media_common.quotation_subject Autonomous agent 02 engineering and technology Machine Learning (cs.LG) Task (project management) Interactive Learning Human–computer interaction 0202 electrical engineering electronic engineering information engineering 0501 psychology and cognitive sciences Computer Science - Multiagent Systems Set (psychology) Reinforcement Function (engineering) 050107 human factors media_common 05 social sciences General Medicine Imitation learning Variety (cybernetics) Artificial Intelligence (cs.AI) 020201 artificial intelligence & image processing Multiagent Systems (cs.MA) |
Zdroj: | AAAI |
Popis: | When deploying autonomous agents in the real world, we need effective ways of communicating objectives to them. Traditional skill learning has revolved around reinforcement and imitation learning, each with rigid constraints on the format of information exchanged between the human and the agent. While scalar rewards carry little information, demonstrations require significant effort to provide and may carry more information than is necessary. Furthermore, rewards and demonstrations are often defined and collected before training begins, when the human is most uncertain about what information would help the agent. In contrast, when humans communicate objectives with each other, they make use of a large vocabulary of informative behaviors, including non-verbal communication, and often communicate throughout learning, responding to observed behavior. In this way, humans communicate intent with minimal effort. In this paper, we propose such interactive learning as an alternative to reward or demonstration-driven learning. To accomplish this, we introduce a multi-agent training framework that enables an agent to learn from another agent who knows the current task. Through a series of experiments, we demonstrate the emergence of a variety of interactive learning behaviors, including information-sharing, information-seeking, and question-answering. Most importantly, we find that our approach produces an agent that is capable of learning interactively from a human user, without a set of explicit demonstrations or a reward function, and achieving significantly better performance cooperatively with a human than a human performing the task alone. AAAI 2020. Video overview at https://youtu.be/8yBvDBuAPrw, paper website with videos and interactive game at http://interactive-learning.github.io/ |
Databáze: | OpenAIRE |
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