| Distributed methods for reinforcement learning survey J Czech Reinforcement Learning Algorithms: Analysis and Applications, 151-161, 2021 | 14 | 2021 |
| Learning to play the chess variant Crazyhouse above world champion level with deep neural networks and human data J Czech, M Willig, A Beyer, K Kersting, J Fürnkranz Frontiers in artificial intelligence 3, 24, 2020 | 14 | 2020 |
| Improving alphazero using monte-carlo graph search J Czech, P Korus, K Kersting Proceedings of the International Conference on Automated Planning and …, 2021 | 8 | 2021 |
| Monte-Carlo graph search for AlphaZero J Czech, P Korus, K Kersting arXiv preprint arXiv:2012.11045, 2020 | 6 | 2020 |
| Generative adversarial neural cellular automata M Otte, Q Delfosse, J Czech, K Kersting arXiv preprint arXiv:2108.04328, 2021 | 4 | 2021 |
| AlphaZe∗∗: AlphaZero-like baselines for imperfect information games are surprisingly strong J Blüml, J Czech, K Kersting Frontiers in artificial intelligence 6, 1014561, 2023 | 2 | 2023 |
| Representation Matters: The Game of Chess Poses a Challenge to Vision Transformers J Czech, J Blüml, K Kersting arXiv preprint arXiv:2304.14918, 2023 | 1 | 2023 |
| Checkmating One, by Using Many: Combining Mixture of Experts with MCTS to Improve in Chess F Helfenstein, J Blüml, J Czech, K Kersting arXiv preprint arXiv:2401.16852, 2024 | | 2024 |
| Know your Enemy: Investigating Monte-Carlo Tree Search with Opponent Models in Pommerman J Weil, J Czech, T Meuser, K Kersting arXiv preprint arXiv:2305.13206, 2023 | | 2023 |
Kristian KerstingProfessor of AI & ML, TU Darmstadt, Co-Director hessian.ai, DFKI, Germany, CLAIRE & ELLISGeverifieerd e-mailadres voor cs.tu-darmstadt.de
