- Tytuł:
- Handling realistic noise in multi-agent systems with self-supervised learning and curiosity
- Autorzy:
-
Szemenyei, Marton
Reizinger, Patrik - Powiązania:
- https://bibliotekanauki.pl/articles/2147129.pdf
- Data publikacji:
- 2022
- Wydawca:
- Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
- Tematy:
-
deep reinforcement learning
multi-agent environment
autonomous driving
robot soccer
self-supervised learning - Opis:
- Most reinforcement learning benchmarks – especially in multi-agent tasks – do not go beyond observations with simple noise; nonetheless, real scenarios induce more elaborate vision pipeline failures: false sightings, misclassifications or occlusion. In this work, we propose a lightweight, 2D environment for robot soccer and autonomous driving that can emulate the above discrepancies. Besides establishing a benchmark for accessible multiagent reinforcement learning research, our work addresses the challenges the simulator imposes. For handling realistic noise, we use self-supervised learning to enhance scene reconstruction and extend curiosity-driven learning to model longer horizons. Our extensive experiments show that the proposed methods achieve state-of-the-art performance, compared against actor-critic methods, ICM, and PPO.
- Źródło:
-
Journal of Artificial Intelligence and Soft Computing Research; 2022, 12, 2; 135--148
2083-2567
2449-6499 - Pojawia się w:
- Journal of Artificial Intelligence and Soft Computing Research
- Dostawca treści:
- Biblioteka Nauki
Artykuł