Deep Q-Network for Angry Birds. - Ekaterina Nikonova; Jakub Gemrot (CoRR, 2019)

Phy-Q: A Benchmark for Physical Reasoning. - Cheng Xue; Vimukthini Pinto; Chathura Nagoda Gamage; Ekaterina Nikonova; Peng Zhang 021; Jochen Renz (CoRR, 2021)

Measuring Difficulty of Novelty Reaction. - Ekaterina Nikonova; Cheng Xue; Vimukthini Pinto; Chathura Nagoda Gamage; Peng Zhang 021; Jochen Renz (CoRR, 2022)

Don't do it: Safer Reinforcement Learning With Rule-based Guidance. - Ekaterina Nikonova; Cheng Xue; Jochen Renz (CoRR, 2022)

NovPhy: A Testbed for Physical Reasoning in Open-world Environments. - Chathura Nagoda Gamage; Vimukthini Pinto; Cheng Xue; Peng Zhang 021; Ekaterina Nikonova; Matthew Stephenson; Jochen Renz (CoRR, 2023)

Efficient Open-world Reinforcement Learning via Knowledge Distillation and Autonomous Rule Discovery. - Ekaterina Nikonova; Cheng Xue; Jochen Renz (CoRR, 2023)

Rapid Open-World Adaptation by Adaptation Principles Learning. - Cheng Xue; Ekaterina Nikonova; Peng Zhang 021; Jochen Renz (CoRR, 2023)

Phy-Q as a measure for physical reasoning intelligence. - Cheng Xue; Vimukthini Pinto; Chathura Nagoda Gamage; Ekaterina Nikonova; Peng Zhang 021; Jochen Renz (Nat. Mac. Intell., 2023)

NovPhy: A physical reasoning benchmark for open-world AI systems. - Vimukthini Pinto; Chathura Nagoda Gamage; Cheng Xue; Peng Zhang 021; Ekaterina Nikonova; Matthew Stephenson; Jochen Renz (Artif. Intell., 2024)