Browsing by Subject "Deep reinforcement learning"
Now showing items 1-6 of 6
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Actor-critic reinforcement learning for bidding in bilateral negotiation
(TÜBİTAK, 2022)Designing an effective and intelligent bidding strategy is one of the most compelling research challenges in automated negotiation, where software agents negotiate with each other to find a mutual agreement when there is ... -
Deep Q-learning based optimization of VLC systems with dynamic time-division multiplexing
(IEEE, 2020)The traditional method to solve nondeterministic-polynomial-time (NP)-hard optimization problems is to apply meta-heuristic algorithms. In contrast, Deep Q Learning (DQL) uses memory of experience and deep neural network ... -
Deep reinforcement based power allocation for the max-min optimization in non-orthogonal multiple access
(IEEE, 2020)NOMA is a radio access technique that multiplexes several users over the frequency resource and provides high throughput and fairness among different users. The maximization of the minimum the data-rate, also known as ... -
Deep reinforcement learning approach for trading automation in the stock market
(IEEE, 2022)Deep Reinforcement Learning (DRL) algorithms can scale to previously intractable problems. The automation of profit generation in the stock market is possible using DRL, by combining the financial assets price 'prediction' ... -
Deep reinforcement learning for acceptance strategy in bilateral negotiations
(TÜBİTAK, 2020)This paper introduces an acceptance strategy based on reinforcement learning for automated bilateral negotiation, where negotiating agents bargain on multiple issues in a variety of negotiation scenarios. Several acceptance ... -
Joint lifetime-outage optimization in relay-enabled IoT networks—A deep reinforcement learning approach
(IEEE, 2023-01)Network lifetime maximization in Internet of things (IoT) is of paramount importance to ensure uninterrupted data transmission and reduce the frequency of battery replacement. This letter deals with the joint lifetime-outage ...
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