375,532 research outputs found
Improved automatic discovery of subgoals for options in hierarchical
Options have been shown to be a key step in extending reinforcement learning beyond low-level reactionary systems to higher-level, planning systems. Most of the options research involves hand-crafted options; there has been only very limited work in the automated discovery of options. We extend early work in automated option discovery with a flexible and robust method.Facultad de Informátic
Improved automatic discovery of subgoals for options in hierarchical
Options have been shown to be a key step in extending reinforcement learning beyond low-level reactionary systems to higher-level, planning systems. Most of the options research involves hand-crafted options; there has been only very limited work in the automated discovery of options. We extend early work in automated option discovery with a flexible and robust method.Facultad de Informátic
Beyond A/B Testing: Sequential Randomization for Developing Interventions in Scaled Digital Learning Environments
Randomized experiments ensure robust causal inference that are critical to
effective learning analytics research and practice. However, traditional
randomized experiments, like A/B tests, are limiting in large scale digital
learning environments. While traditional experiments can accurately compare two
treatment options, they are less able to inform how to adapt interventions to
continually meet learners' diverse needs. In this work, we introduce a trial
design for developing adaptive interventions in scaled digital learning
environments -- the sequential randomized trial (SRT). With the goal of
improving learner experience and developing interventions that benefit all
learners at all times, SRTs inform how to sequence, time, and personalize
interventions. In this paper, we provide an overview of SRTs, and we illustrate
the advantages they hold compared to traditional experiments. We describe a
novel SRT run in a large scale data science MOOC. The trial results
contextualize how learner engagement can be addressed through inclusive
culturally targeted reminder emails. We also provide practical advice for
researchers who aim to run their own SRTs to develop adaptive interventions in
scaled digital learning environments
Fatalism and Savings
We examine the impact of fatalism, the belief that one has little or no control over future events, on the decision of whether or not to save. We develop a model that predicts that fatalism decreases savings for moderately risk averse individuals, increases savings for highly risk averse individuals, and otherwise has no impact. Furthermore, fatalism decreases effort in learning about savings and investment options. We use data from National Longitudinal Survey of Youth (NLSY) and find general support for the theoretical predictions of the model. The results are robust to the inclusion of a number of additional control variables.fatalism, savings, risk aversion
Fuzzy Logic Path Planning System for Collision Avoidance by an Autonomous Rover Vehicle
Systems already developed at JSC have shown the benefits of applying fuzzy logic control theory to space related operations. Four major issues are addressed that are associated with developing an autonomous collision avoidance subsystem within a path planning system designed for application in a remote, hostile environment that does not lend itself well to remote manipulation of the vehicle involved through Earth-based telecommunication. A good focus for this is unmanned exploration of the surface of Mars. The uncertainties involved indicate that robust approaches such as fuzzy logic control are particularly appropriate. The four major issues addressed are: (1) avoidance of a single fuzzy moving obstacle; (2) back off from a dead end in a static obstacle environment; (3) fusion of sensor data to detect obstacles; and (4) options for adaptive learning in a path planning system
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