| Vizdoom: A doom-based ai research platform for visual reinforcement learning M Kempka, M Wydmuch, G Runc, J Toczek, W Jaśkowski 2016 IEEE conference on computational intelligence and games (CIG), 1-8, 2016 | 886 | 2016 |
| Genetic programming needs better benchmarks J McDermott, DR White, S Luke, L Manzoni, M Castelli, L Vanneschi, ... Proceedings of the 14th annual conference on Genetic and evolutionary …, 2012 | 323 | 2012 |
| Better GP benchmarks: community survey results and proposals DR White, J McDermott, M Castelli, L Manzoni, BW Goldman, ... Genetic Programming and Evolvable Machines 14, 3-29, 2013 | 277 | 2013 |
| Model-based Active Exploration P Shyam, W Jaśkowski, F Gomez arXiv preprint arXiv:1810.12162, 2018 | 233 | 2018 |
| ViZDoom Competitions: Playing Doom From Pixels M Wydmuch, M Kempka, W Jaśkowski IEEE Transactions on Games 11 (3), 248-259, 2018 | 165 | 2018 |
| Training agents using upside-down reinforcement learning RK Srivastava, P Shyam, F Mutz, W Jaśkowski, J Schmidhuber arXiv preprint arXiv:1912.02877, 2019 | 132 | 2019 |
| Temporal difference learning of n-tuple networks for the game 2048 M Szubert, W Jaśkowski 2014 IEEE Conference on Computational Intelligence and Games, 1-8, 2014 | 67 | 2014 |
| Artificial intelligence for prosthetics: Challenge solutions Ł Kidziński, C Ong, SP Mohanty, J Hicks, S Carroll, B Zhou, H Zeng, ... The NeurIPS'18 Competition: From Machine Learning to Intelligent …, 2020 | 53 | 2020 |
| The influence of motor imagery on the learning of a fine hand motor skill J Sobierajewicz, A Przekoracka-Krawczyk, W Jaśkowski, WB Verwey, ... Experimental brain research 235, 305-320, 2017 | 50 | 2017 |
| Mastering 2048 with delayed temporal coherence learning, multistage weight promotion, redundant encoding, and carousel shaping W Jaśkowski IEEE Transactions on Games 10 (1), 3-14, 2017 | 44 | 2017 |
| Learning to Play Othello with Deep Neural Networks P Liskowski, W Jaśkowski, K Krawiec IEEE Transactions on Games 10 (4), 354-364, 2018 | 40 | 2018 |
| How to learn a useful critic? model-based action-gradient-estimator policy optimization P D'Oro, W Jaśkowski Advances in Neural Information Processing Systems 33, 313-324, 2020 | 33 | 2020 |
| Coevolutionary temporal difference learning for Othello M Szubert, W Jaskowski, K Krawiec Computational Intelligence and Games, 2009. CIG 2009. IEEE Symposium on, 104-111, 2009 | 33 | 2009 |
| Heterogeneous team deep Q-learning in low-dimensional multi-agent environments M Kurek, W Jaśkowski 2016 IEEE Conference on Computational Intelligence and Games (CIG), 1-8, 2016 | 31 | 2016 |
| On scalability, generalization, and hybridization of coevolutionary learning: a case study for othello M Szubert, W Jaśkowski, K Krawiec IEEE Transactions on Computational Intelligence and AI in Games 5 (3), 214-226, 2013 | 28 | 2013 |
| Formal analysis, hardness, and algorithms for extracting internal structure of test-based problems W Jaśkowski, K Krawiec Evolutionary computation 19 (4), 639-671, 2011 | 25 | 2011 |
| Do musicians learn a fine sequential hand motor skill differently than non-musicians? J Sobierajewicz, R Naskręcki, W Jaśkowski, RHJ Van der Lubbe Plos one 13 (11), e0207449, 2018 | 24 | 2018 |
| Reinforcement learning to run… fast W Jaśkowski, OR Lykkebø, NE Toklu, F Trifterer, Z Buk, J Koutník, ... The NIPS'17 Competition: Building Intelligent Systems, 155-167, 2018 | 23 | 2018 |
| Winning ant wars: Evolving a human-competitive game strategy using fitnessless selection W Jaśkowski, K Krawiec, B Wieloch Genetic Programming, 13-24, 2008 | 23 | 2008 |
| To what extent can motor imagery replace motor execution while learning a fine motor skill? J Sobierajewicz, S Szarkiewicz, A Przekoracka-Krawczyk, W Jaśkowski, ... Advances in Cognitive Psychology 12 (4), 179, 2016 | 22 | 2016 |
Krzysztof KrawiecProfessor of Computer Science, Poznan University of Technology確認したメール アドレス: cs.put.poznan.pl