Sequential decision making for lifetime maximization is a critical problem in many real-world applications, such as medical treatment and portfolio selection. In these applications, a "reneging" phenomenon, where participants may disengage from future interactions after observing an unsatisfiable outcome, is rather pre…
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4 results for “reneging”
Study shows how learning and analytical models affect reneging and jockeying in a dual M/M/1 system.
problem How do learning and analytical models affect reneging and jockeying in a dual M/M/1 system?
method Analytical and online trained actor-critic models were used to study reneging and jockeying in a dual M/M/1 system.
result Both analytical and online trained actor-critic models yield the same asymptotic limits for reneging and jockeying, but differ in practical sizes.
In autonomous vehicle (AV) control, allowing mistakes can be quite dangerous and costly in the real world. For this reason we investigate methods of training an AV without allowing the agent to explore and instead having a human explorer collect the data. Supervised learning has been explored for AV control, but it enc…
Deep neural networks have excelled on a wide range of problems, from vision to language and game playing. Neural networks very gradually incorporate information into weights as they process data, requiring very low learning rates. If the training distribution shifts, the network is slow to adapt, and when it does adapt…