On the Utility of Learning about Humans for Human-AI Coordination . On the Utility of Learning about Humans for Human-AI Coordination. Micah Carroll, Rohin Shah, Mark K. Ho, Thomas L. Griffiths, Sanjit A. Seshia, Pieter Abbeel, Anca.
On the Utility of Learning about Humans for Human-AI Coordination from images.deepai.org
An experiment with a planning algorithm yields the same conclusion, though only when the human-aware planner is given the exact human model that it is playing with. A user.
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we only use imitation learning to create human models, and train our agent using pure RL. Imitation learning. Imitation learning [1, 16] aims to train agents that mimic the policies of a.
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(In fact, in the second setting of Asymmetric Advantages in Figure 3(a), the human-AI team beats the AI-AI team, suggesting that the role played by the human is hard to.
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A simple environment that requires challenging coordination, based on the popular game Overcooked, is introduced and a simple model is learned that mimics human.
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Human-Aware Reinforcement Learning. This code is based on the work in On the Utility of Learning about Humans for Human-AI Coordination. Contents. To play the game.
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come from the AI system’s individual ability, rather than from coordination with humans. We claim that in general, collaboration is fundamentally different from competition, and will require.
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H3. When partnered with a human, a human-aware agent will achieve higher performance than an agent trained via imitation learning. They also created the Overcooked.
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In contrast, human coordination protocols are likely much more general. This suggests that we could make AI protocols similar to human ones by forcing the AI protocols to.
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Abstract. While we would like agents that can coordinate with humans, current algorithms such as self-play and population-based training create agents that can coordinate with themselves..
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People; More. Search ACM Digital Library. Search Search.. Home Browse by Title NIPS'19 On the utility of learning about humans for human-AI coordination. research-article . Free.
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On the Utility of Learning about Humans for Human-AI Coordination. While we would like agents that can coordinate with humans, current algorithms such as self-play and population.
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Agents that assume their partner to be optimal or similar to them can converge to coordination protocols that fail to understand and be understood by humans. To demonstrate this, we.
