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ORCIDAlessandro Sestini
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Journal Articles
- 2024
[j1]Alessandro Sestini
, Linus Gisslén, Joakim Bergdahl, Konrad Tollmar, Andrew D. Bagdanov:
Automated Gameplay Testing and Validation With Curiosity-Conditioned Proximal Trajectories. IEEE Trans. Games 16(1): 113-126 (2024)
Conference and Workshop Papers
- 2024
- [c10]Derek Yadgaroff, Alessandro Sestini, Konrad Tollmar, Ayça Özçelikkale, Linus Gisslén:
Improving Generalization in Game Agents with Data Augmentation in Imitation Learning. CEC 2024: 1-8 - [c9]Joakim Bergdahl, Alessandro Sestini, Linus Gisslén:
Reinforcement Learning for High-Level Strategic Control in Tower Defense Games. CoG 2024: 1-8 - [c8]Girolamo Macaluso, Alessandro Sestini, Andrew D. Bagdanov:
A Benchmark Environment for Offline Reinforcement Learning in Racing Games. CoG 2024: 1-4 - [c7]Leonardo Marini, Linus Gisslén, Alessandro Sestini:
Leveraging Large Language Models for Efficient Failure Analysis in Game Development. CoG 2024: 1-8 - 2023
- [c6]William Ahlberg, Alessandro Sestini, Konrad Tollmar, Linus Gisslén:
Generating Personas for Games with Multimodal Adversarial Imitation Learning. CoG 2023: 1-8 - [c5]Jonas Gillberg, Joakim Bergdahl, Alessandro Sestini, Andrew Eakins, Linus Gisslén:
Technical Challenges of Deploying Reinforcement Learning Agents for Game Testing in AAA Games. CoG 2023: 1-8 - [c4]Rodrigue de Schaetzen, Alessandro Sestini:
Efficient Ground Vehicle Path Following in Game AI. CoG 2023: 1-4 - [c3]Alessandro Sestini, Joakim Bergdahl, Konrad Tollmar, Andrew D. Bagdanov, Linus Gisslén:
Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learning. CoG 2023: 1-8 - 2021
- [c2]Alessandro Sestini, Alexander Kuhnle, Andrew D. Bagdanov:
Demonstration-Efficient Inverse Reinforcement Learning in Procedurally Generated Environments. CoG 2021: 1-8 - [c1]Alessandro Sestini, Alexander Kuhnle, Andrew D. Bagdanov:
Policy Fusion for Adaptive and Customizable Reinforcement Learning Agents. CoG 2021: 1-8
Informal and Other Publications
- 2024
- [i16]Leonardo Marini, Linus Gisslén, Alessandro Sestini:
Leveraging Large Language Models for Efficient Failure Analysis in Game Development. CoRR abs/2406.07084 (2024) - [i15]Joakim Bergdahl, Alessandro Sestini, Linus Gisslén:
Reinforcement Learning for High-Level Strategic Control in Tower Defense Games. CoRR abs/2406.07980 (2024) - [i14]Girolamo Macaluso, Alessandro Sestini, Andrew D. Bagdanov:
A Benchmark Environment for Offline Reinforcement Learning in Racing Games. CoRR abs/2407.09415 (2024) - [i13]Monica Villanueva Aylagas, Joakim Bergdahl, Jonas Gillberg, Alessandro Sestini, Theodor Tolstoy, Linus Gisslén:
Improving Conditional Level Generation using Automated Validation in Match-3 Games. CoRR abs/2409.06349 (2024) - 2023
- [i12]Rodrigue de Schaetzen, Alessandro Sestini:
Efficient Ground Vehicle Path Following in Game AI. CoRR abs/2307.03379 (2023) - [i11]Jonas Gillberg, Joakim Bergdahl, Alessandro Sestini, Andrew Eakins, Linus Gisslén:
Technical Challenges of Deploying Reinforcement Learning Agents for Game Testing in AAA Games. CoRR abs/2307.11105 (2023) - [i10]William Ahlberg, Alessandro Sestini, Konrad Tollmar, Linus Gisslén:
Generating Personas for Games with Multimodal Adversarial Imitation Learning. CoRR abs/2308.07598 (2023) - [i9]Derek Yadgaroff, Alessandro Sestini, Konrad Tollmar, Linus Gisslén:
Improving Generalization in Game Agents with Data Augmentation in Imitation Learning. CoRR abs/2309.12815 (2023) - [i8]Girolamo Macaluso, Alessandro Sestini, Andrew D. Bagdanov:
Small Dataset, Big Gains: Enhancing Reinforcement Learning by Offline Pre-Training with Model Based Augmentation. CoRR abs/2312.09844 (2023) - 2022
- [i7]Alessandro Sestini, Linus Gisslén, Joakim Bergdahl, Konrad Tollmar, Andrew D. Bagdanov:
CCPT: Automatic Gameplay Testing and Validation with Curiosity-Conditioned Proximal Trajectories. CoRR abs/2202.10057 (2022) - [i6]Tommaso Aldinucci, Enrico Civitelli, Leonardo Di Gangi, Alessandro Sestini:
Contextual Decision Trees. CoRR abs/2207.06355 (2022) - [i5]Alessandro Sestini, Joakim Bergdahl, Konrad Tollmar, Andrew D. Bagdanov, Linus Gisslén:
Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learning. CoRR abs/2208.07811 (2022) - 2021
- [i4]Alessandro Sestini, Alexander Kuhnle, Andrew D. Bagdanov:
Policy Fusion for Adaptive and Customizable Reinforcement Learning Agents. CoRR abs/2104.10610 (2021) - 2020
- [i3]Alessandro Sestini, Alexander Kuhnle, Andrew D. Bagdanov:
DeepCrawl: Deep Reinforcement Learning for Turn-based Strategy Games. CoRR abs/2012.01914 (2020) - [i2]Alessandro Sestini, Alexander Kuhnle, Andrew D. Bagdanov:
Demonstration-efficient Inverse Reinforcement Learning in Procedurally Generated Environments. CoRR abs/2012.02527 (2020) - [i1]Alessandro Sestini, Alexander Kuhnle, Andrew D. Bagdanov:
Deep Policy Networks for NPC Behaviors that Adapt to Changing Design Parameters in Roguelike Games. CoRR abs/2012.03532 (2020)
Coauthor Index
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