1 code implementation • 2 May 2023 • Emma Stensby Norstein, Kai Olav Ellefsen, Kyrre Glette
We want to move one step closer to creating simulated environments similar to the diverse real world, where agents can both find solvable tasks, and adapt to them.
1 code implementation • 7 Apr 2021 • Emma Hjellbrekke Stensby, Kai Olav Ellefsen, Kyrre Glette
We compare the diversity, fitness and robustness of agents evolving in environments generated by POET to agents evolved in handcrafted curricula of environments.
no code implementations • 29 Jan 2021 • Bjørn Ivar Teigen, Neil Davies, Kai Olav Ellefsen, Tor Skeie, Jim Torresen
Instead of computing throughput numbers from a steady-state analysis of a Markov chain, we explicitly model latency and packet loss.
Networking and Internet Architecture Performance C.2.2; C.2.5; C.4
no code implementations • 5 Aug 2020 • Jørgen Nordmoen, Frank Veenstra, Kai Olav Ellefsen, Kyrre Glette
In this paper we compare a single objective Evolutionary Algorithm with two diversity promoting search algorithms; a Multi-Objective Evolutionary Algorithm and MAP-Elites a Quality Diversity algorithm, for the difficult problem of evolving control and morphology in modular robotics.
no code implementations • 4 Apr 2019 • Kai Olav Ellefsen, Jim Torresen
In this paper, we extend a recent deep learning architecture which learns a predictive model of the environment that aims to predict only the value of a few key measurements, which are be indicative of an agent's performance.
Model-based Reinforcement Learning reinforcement-learning +1
no code implementations • 12 Feb 2019 • Kai Olav Ellefsen, Joost Huizinga, Jim Torresen
However, on a problem where the optimal decomposition is less obvious, the structural diversity objective is found to outcompete other structural objectives -- and this technique can even increase performance on problems without any decomposable structure at all.
no code implementations • 23 Jan 2019 • Kai Olav Ellefsen, Charles Patrick Martin, Jim Torresen
Gaining a better understanding of how and what machine learning systems learn is important to increase confidence in their decisions and catalyze further research.
no code implementations • 22 Jan 2019 • Kai Olav Ellefsen, Herman A. Lepikson, Jan C. Albiez
We here test our method on a set of inspection targets with large variation in size and complexity, and compare its performance with two state-of-the-art methods for complete coverage path planning.
no code implementations • 9 Mar 2018 • Joel Lehman, Jeff Clune, Dusan Misevic, Christoph Adami, Lee Altenberg, Julie Beaulieu, Peter J. Bentley, Samuel Bernard, Guillaume Beslon, David M. Bryson, Patryk Chrabaszcz, Nick Cheney, Antoine Cully, Stephane Doncieux, Fred C. Dyer, Kai Olav Ellefsen, Robert Feldt, Stephan Fischer, Stephanie Forrest, Antoine Frénoy, Christian Gagné, Leni Le Goff, Laura M. Grabowski, Babak Hodjat, Frank Hutter, Laurent Keller, Carole Knibbe, Peter Krcah, Richard E. Lenski, Hod Lipson, Robert MacCurdy, Carlos Maestre, Risto Miikkulainen, Sara Mitri, David E. Moriarty, Jean-Baptiste Mouret, Anh Nguyen, Charles Ofria, Marc Parizeau, David Parsons, Robert T. Pennock, William F. Punch, Thomas S. Ray, Marc Schoenauer, Eric Shulte, Karl Sims, Kenneth O. Stanley, François Taddei, Danesh Tarapore, Simon Thibault, Westley Weimer, Richard Watson, Jason Yosinski
Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them.
no code implementations • 31 Jan 2018 • Charles P. Martin, Kai Olav Ellefsen, Jim Torresen
Musical performance requires prediction to operate instruments, to perform in groups and to improvise.