98 citations · 339 across the 69 of their papers we have counts for
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Towards a Deep Reinforcement Learning Approach for Tower Line Wars
Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
There have been numerous breakthroughs with reinforcement learning in the recent years, perhaps most notably on Deep Reinforcement Learning successfully playing and winning relativ…
Thompson Sampling Guided Stochastic Searching on the Line for Deceptive Environments with Applications to Root-Finding Problems
Sondre Glimsdal, Ole-Christoffer Granmo
The multi-armed bandit problem forms the foundation for solving a wide range of on-line stochastic optimization problems through a simple, yet effective mechanism. One simply casts…
An Optimal Bayesian Network Based Solution Scheme for the Constrained Stochastic On-line Equi-Partitioning Problem
Sondre Glimsdal, Ole-Christoffer Granmo
A number of intriguing decision scenarios revolve around partitioning a collection of objects to optimize some application specific objective function. This problem is generally re…
Bayesian Unification of Gradient and Bandit-based Learning for Accelerated Global Optimisation
Ole-Christoffer Granmo
Bandit based optimisation has a remarkable advantage over gradient based approaches due to their global perspective, which eliminates the danger of getting stuck at local optima. H…