10 citations · 28 across the 9 of their papers we have counts for
9 papers
Towards Learning Rubik's Cube with N-tuple-based Reinforcement Learning
Wolfgang Konen
This work describes in detail how to learn and solve the Rubik's cube game (or puzzle) in the General Board Game (GBG) learning and playing framework. We cover the cube sizes 2x2x2…
AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time
Johannes Scheiermann, Wolfgang Konen
Recently, the seminal algorithms AlphaGo and AlphaZero have started a new era in game learning and deep reinforcement learning. While the achievements of AlphaGo and AlphaZero - pl…
General Board Game Playing for Education and Research in Generic AI Game Learning
Wolfgang Konen
We present a new general board game (GBG) playing and learning framework. GBG defines the common interfaces for board games, game states and their AI agents. It allows one to run c…
SACOBRA with Online Whitening for Solving Optimization Problems with High Conditioning
Samineh Bagheri, Wolfgang Konen, Thomas Bäck
Real-world optimization problems often have expensive objective functions in terms of cost and time. It is desirable to find near-optimal solutions with very few function evaluatio…
Illumination-invariant image mosaic calculation based on logarithmic search
Wolfgang Konen
This technical report describes an improved image mosaicking algorithm. It is based on Jain's logarithmic search algorithm [Jain 1981] which is coupled to the method of Kourogi (19…
Solving the G-problems in less than 500 iterations: Improved efficient constrained optimization by surrogate modeling and adaptive parameter control
Samineh Bagheri, Wolfgang Konen, Michael Emmerich +1
Constrained optimization of high-dimensional numerical problems plays an important role in many scientific and industrial applications. Function evaluations in many industrial appl…