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Final Adaptation Reinforcement Learning for N-Player Games
Our main contribution is that FARL is a vitally important ingredient to achieve success with the player-centered view in various games.
SACOBRA with Online Whitening for Solving Optimization Problems with High Conditioning
We show on a set of high-conditioning functions that online whitening tackles SACOBRA's early stagnation issue and reduces the optimization error by a factor between 10 to 1e12 as compared to the plain SACOBRA, though it imposes many extra function evaluations.
Solving the G-problems in less than 500 iterations: Improved efficient constrained optimization by surrogate modeling and adaptive parameter control
We analyze the importance of the several new elements in SACOBRA and find that each element of SACOBRA plays a role to boost up the overall optimization performance.
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