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Activity Number:
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10
- Agent-Based Models for Informing Public Policy: Applications and Statistical Challenges
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Type:
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Invited
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Date/Time:
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Monday, August 3, 2020 : 10:00 AM to 11:50 AM
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Sponsor:
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Social Statistics Section
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Abstract #309275
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Title:
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Inverse Reinforcement Learning for Agent Based Models
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Author(s):
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Toryn Schafer* and Christopher Wikle
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Companies:
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University of Missouri and University of Missouri
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Keywords:
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reinforcement learning;
behavior trajectory;
Bayesian;
uncertainty quantification
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Abstract:
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Agent-based methods allow for defining simple rules that generate complex group behaviors. The governing rules of such models are typically set a priori and parameters are learned from observed behavior trajectories. Instead of making simplifying assumptions across all anticipated scenarios, inverse reinforcement learning provides inference on the short-term (local) rules governing long term behavior policies by using properties of a Markov decision process. We learn rewards using variational approximation and provide estimates of uncertainty.
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Authors who are presenting talks have a * after their name.
