Results 41 to 50 of about 241,763 (280)
Q-learning based strategy analysis of cyber-physical systems considering unequal cost
Intelligent and Converged Networks, 2023 This paper proposes a cyber security strategy for cyber-physical systems (CPS) based on Q-learning under unequal cost to obtain a more efficient and low-cost cyber security defense strategy with misclassification interference.
Xin Chen +5 more
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Q-LVS: A Q-Learning-based Algorithm for Video Streaming in Peer-to-Peer Networks Considering a Token-Based Incentive Mechanism [PDF]
Journal of Artificial Intelligence and Data Mining, 2022 Peer-to-peer video streaming has reached great attention during recent years. Video streaming in peer-to-peer networks is a good way to stream video on the Internet due to the high scalability, high video quality, and low bandwidth requirements.
Z. Imanimehr
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IEEE Access, 2023 The current application of control theory is commonly carried out in systems with a model or known system dynamics. However, in practice this is a formidable task to achieve as not all state information can be known. The use of the Output Feedback (OPFB)
Adi Novitarini Putri +3 more
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Assessing the Potential of Classical Q-learning in General Game Playing [PDF]
, 2018 After the recent groundbreaking results of AlphaGo and AlphaZero, we have seen strong interests in deep reinforcement learning and artificial general intelligence (AGI) in game playing.
CB Browne +12 more
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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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Learning Negotiating Behavior Between Cars in Intersections using Deep Q-Learning [PDF]
, 2018 This paper concerns automated vehicles negotiating with other vehicles, typically human driven, in crossings with the goal to find a decision algorithm by learning typical behaviors of other vehicles.
Ali, Mohammad +4 more
core +2 more sources
Advances in Neural Information Processing Systems 37Q-learning is widely used algorithm in reinforcement learning community. Under the lookup table setting, its convergence is well established. However, its behavior is known to be unstable with the linear function approximation case. This paper develops a new Q-learning algorithm that converges when linear function approximation is used.
Lim, Han-Dong, Lee, Donghwan
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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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Successive Over-Relaxation ${Q}$ -Learning [PDF]
IEEE Control Systems Letters, 2020 In a discounted reward Markov Decision Process (MDP), the objective is to find the optimal value function, i.e., the value function corresponding to an optimal policy. This problem reduces to solving a functional equation known as the Bellman equation and a fixed point iteration scheme known as the value iteration is utilized to obtain the solution. In
Chandramouli Kamanchi +2 more
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