Machine Teaching for Human Inverse Reinforcement Learning [PDF]
Frontiers in Robotics and AI, 2021 As robots continue to acquire useful skills, their ability to teach their expertise will provide humans the two-fold benefit of learning from robots and collaborating fluently with them.
Michael S. Lee +2 more
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Bayesian multitask inverse reinforcement learning [PDF]
, 2011 We generalise the problem of inverse reinforcement learning to multiple tasks, from multiple demonstrations. Each one may represent one expert trying to solve a different task, or as different experts trying to solve the same task.
C.A. Rothkopf +3 more
core +6 more sources
Integrating inverse reinforcement learning into data-driven mechanistic computational models: a novel paradigm to decode cancer cell heterogeneity [PDF]
Frontiers in Systems BiologyCellular heterogeneity is a ubiquitous aspect of biology and a major obstacle to successful cancer treatment. Several techniques have emerged to quantify heterogeneity in live cells along axes including cellular migration, morphology, growth, and ...
Patrick C. Kinnunen +17 more
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Inverse Reinforcement Learning for Marketing [PDF]
SSRN Electronic Journal, 2017 Learning customer preferences from an observed behaviour is an important topic in the marketing literature. Structural models typically model forward-looking customers or firms as utility-maximizing agents whose utility is estimated using methods of ...
Halperin, Igor
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Identification of animal behavioral strategies by inverse reinforcement learning. [PDF]
PLoS Computational Biology, 2018 Animals are able to reach a desired state in an environment by controlling various behavioral patterns. Identification of the behavioral strategy used for this control is important for understanding animals' decision-making and is fundamental to dissect ...
Shoichiro Yamaguchi +6 more
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Stochastic Inverse Reinforcement Learning
, 2020 The goal of the inverse reinforcement learning (IRL) problem is to recover the reward functions from expert demonstrations. However, the IRL problem like any ill-posed inverse problem suffers the congenital defect that the policy may be optimal for many ...
Ju, Ce
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Culturally-attuned AI: Implicit learning of altruistic cultural values through inverse reinforcement learning. [PDF]
PLoS ONEConstructing a universal moral code for artificial intelligence (AI) is challenging because human cultures have different values, norms, and social practices.
Nigini Oliveira +6 more
doaj +2 more sources
Neural computations underlying inverse reinforcement learning in the human brain [PDF]
eLife, 2017 In inverse reinforcement learning an observer infers the reward distribution available for actions in the environment solely through observing the actions implemented by another agent.
Sven Collette +3 more
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Analyzing Sensor-Based Individual and Population Behavior Patterns via Inverse Reinforcement Learning [PDF]
Sensors, 2020 Digital markers of behavior can be continuously created, in everyday settings, using time series data collected by ambient sensors. The goal of this work was to perform individual- and population-level behavior analysis from such time series sensor data.
Beiyu Lin, Diane J. Cook
doaj +2 more sources
Bayesian Nonparametric Inverse Reinforcement Learning [PDF]
, 2012 Inverse reinforcement learning (IRL) is the task of learning the reward function of a Markov Decision Process (MDP) given the transition function and a set of observed demonstrations in the form of state-action pairs.
B.D. Argall +10 more
core +7 more sources