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Convergence Of A Reinforcement Learning Algorithm In Continuous Domains, Stephen Carden
Convergence Of A Reinforcement Learning Algorithm In Continuous Domains, Stephen Carden
All Dissertations
In the field of Reinforcement Learning, Markov Decision Processes with a finite number of states and actions have been well studied, and there exist algorithms capable of producing a sequence of policies which converge to an optimal policy with probability one. Convergence guarantees for problems with continuous states also exist. Until recently, no online algorithm for continuous states and continuous actions has been proven to produce optimal policies. This Dissertation contains the results of research into reinforcement learning algorithms for problems in which both the state and action spaces are continuous. The problems to be solved are introduced formally as …
How Many Eyeballs Does A Bug Need? An Empirical Validation Of Linus' Law, Subhajit Datta, Proshanta Sarkar, Sutirtha Das, Sonu Sreshtha, Prasanth Lade, Subhashis Majumder
How Many Eyeballs Does A Bug Need? An Empirical Validation Of Linus' Law, Subhajit Datta, Proshanta Sarkar, Sutirtha Das, Sonu Sreshtha, Prasanth Lade, Subhashis Majumder
Research Collection School Of Computing and Information Systems
Linus’ Law reflects on a key characteristic of open source software development: developers’ tendency to closely work together in the bug resolution process. In this paper we empirically examine Linus’ Law using a data-set of 1,000+ Android bugs, owned by 70+ developers. Our results indicate that encouraging developers to work closely with one another has nuanced implications; while one form of contact may help reduce bug resolution time, another form can have quite the opposite effect. We present statistically significant evidence in support of our results and discuss their relevance at the individual and organizational levels.