Zobrazeno 1 - 10
of 788
pro vyhledávání: '"Dogan C"'
A widely-studied deep reinforcement learning (RL) technique known as Prioritized Experience Replay (PER) allows agents to learn from transitions sampled with non-uniform probability proportional to their temporal-difference (TD) error. Although it ha
Externí odkaz:
http://arxiv.org/abs/2209.00532
Compared to on-policy counterparts, off-policy model-free deep reinforcement learning can improve data efficiency by repeatedly using the previously gathered data. However, off-policy learning becomes challenging when the discrepancy between the unde
Externí odkaz:
http://arxiv.org/abs/2208.00755
Learning in high dimensional continuous tasks is challenging, mainly when the experience replay memory is very limited. We introduce a simple yet effective experience sharing mechanism for deterministic policies in continuous action domains for the f
Externí odkaz:
http://arxiv.org/abs/2207.13453
Autor:
Cicek, Dogan C., Duran, Enes, Saglam, Baturay, Kaya, Kagan, Mutlu, Furkan B., Kozat, Suleyman S.
Value-based deep Reinforcement Learning (RL) algorithms suffer from the estimation bias primarily caused by function approximation and temporal difference (TD) learning. This problem induces faulty state-action value estimates and therefore harms the
Externí odkaz:
http://arxiv.org/abs/2111.06780
The experience replay mechanism allows agents to use the experiences multiple times. In prior works, the sampling probability of the transitions was adjusted according to their importance. Reassigning sampling probabilities for every transition in th
Externí odkaz:
http://arxiv.org/abs/2111.01865
In value-based deep reinforcement learning methods, approximation of value functions induces overestimation bias and leads to suboptimal policies. We show that in deep actor-critic methods that aim to overcome the overestimation bias, if the reinforc
Externí odkaz:
http://arxiv.org/abs/2109.10736
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
Revista Electrónica de LEEME. 2023, Issue 52, p18-38. 20p.
Publikováno v:
In Pulmonology September-October 2020 26(5):275-282
Publikováno v:
In Allergologia et Immunopathologia September-October 2020 48(5):430-440