Results 31 to 40 of about 212,805 (267)
, 2020 This work has received funding from the EU Horizon 2020 research and innovation program under project DOMINOES (grant agreement No 771066) and from FEDER Funds through COMPETE program and from National Funds through FCT under projects CEECIND/01811/2017 and UIDB/00760 ...
Vale, Zita, Pinto, Tiago
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CoRR, 2018 Distributional reinforcement learning (distributional RL) has seen empirical success in complex Markov Decision Processes (MDPs) in the setting of nonlinear function approximation. However, there are many different ways in which one can leverage the distributional approach to reinforcement learning.
Thang Doan, Bogdan Mazoure, Clare Lyle
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Maxmin Q-learning: Controlling the Estimation Bias of Q-learning
CoRR, 2020 ICLR ...
Qingfeng Lan +3 more
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Sparse cooperative Q-learning [PDF]
Twenty-first international conference on Machine learning - ICML '04, 2004 Learning in multiagent systems suffers from the fact that both the state and the action space scale exponentially with the number of agents. In this paper we are interested in using Q-learning to learn the coordinated actions of a group of cooperative agents, using a sparse representation of the joint state-action space of the agents.
Kok, J.R., Vlassis, N.
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OPTIMIZING QOS IN SELF ORGANIZING HETEROGENEOUS WIRELESS CELLULAR NETWORK USING FIREFLY ALGORITHM
ICTACT Journal on Communication Technology, 2022 Capacity and energy efficiency are crucial for next-generation wireless networks. Due to the dense deployment of base stations (BSs) in a heterogeneous network (HetNets), the consumption is from 60% to 80% of the total energy causing accentuated costs ...
Gajanan Uttam Patil +1 more
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Continuous-Action Q-Learning [PDF]
Machine Learning, 2002 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
José del R. Millán +2 more
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CoRR, 2020 To appear in proceedings of the 37th International Conference on Machine ...
Ibrahim El Shar, Daniel R. Jiang
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Frontiers in Cell and Developmental Biology, 2022 Calculating and predicting drug-target interactions (DTIs) is a crucial step in the field of novel drug discovery. Nowadays, many models have improved the prediction performance of DTIs by fusing heterogeneous information, such as drug chemical structure
Jiacheng Sun +14 more
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Uncertainty-aware Path Planning using Reinforcement Learning and Deep Learning Methods [PDF]
Computer and Knowledge Engineering, 2020 This paper proposes new algorithms to improve Reinforcement Learning (RL) and Deep Q-Network (DQN) methods for path planning considering uncertainty in the perception of environment.
Nematollah Ab azar +2 more
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