Temat: reinforcement 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:: Discrete uncertainty quantification for offline reinforcement learning
Autorzy:: Pérez Torres, Jose Luis
Corrochano Jiménez, Javier
García, Javier
Majadas, Rubén
Ibañez-Llano, Cristina
Pérez, Sergio
Fernández, Fernando
Powiązania:: https://bibliotekanauki.pl/articles/23944835.pdf
Data publikacji:: 2023
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: off-line reinforcement learning
uncertainty quantification
machine learning
Opis:: In many Reinforcement Learning (RL) tasks, the classical online interaction of the learning agent with the environment is impractical, either because such interaction is expensive or dangerous. In these cases, previous gathered data can be used, arising what is typically called Offline RL. However, this type of learning faces a large number of challenges, mostly derived from the fact that exploration/exploitation trade-off is overshadowed. In addition, the historical data is usually biased by the way it was obtained, typically, a sub-optimal controller, producing a distributional shift from historical data and the one required to learn the optimal policy. In this paper, we present a novel approach to deal with the uncertainty risen by the absence or sparse presence of some state-action pairs in the learning data. Our approach is based on shaping the reward perceived from the environment to ensure the task is solved. We present the approach and show that combining it with classic online RL methods make them perform as good as state of the art Offline RL algorithms such as CQL and BCQ. Finally, we show that using our method on top of established offline learning algorithms can improve them.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 4; 273--287
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: 3.

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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