Temat: agent learning - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Multi agent deep learning with cooperative communication
Autorzy:: Simões, David
Lau, Nuno
Reis, Luís Paulo
Powiązania:: https://bibliotekanauki.pl/articles/1837537.pdf
Data publikacji:: 2020
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: multi-agent systems
deep reinforcement learning
centralized learning
Opis:: We consider the problem of multi agents cooperating in a partially-observable environment. Agents must learn to coordinate and share relevant information to solve the tasks successfully. This article describes Asynchronous Advantage Actor-Critic with Communication (A3C2), an end-to-end differentiable approach where agents learn policies and communication protocols simultaneously. A3C2 uses a centralized learning, distributed execution paradigm, supports independent agents, dynamic team sizes, partiallyobservable environments, and noisy communications. We compare and show that A3C2 outperforms other state-of-the-art proposals in multiple environments.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 3; 189-207
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

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

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