Results 31 to 40 of about 37,169,246 (294)
Computational Psychiatry, 2022 Gambling disorder is a behavioral addiction that negatively impacts personal finances, work, relationships and mental health. In this pre-registered study (https://osf.io/5ptz9/) we investigated the impact of real-life gambling environments on two ...
Ben Wagner, David Mathar, Jan Peters
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Benchmarking Model-Based Reinforcement Learning
CoRR, 2019 8 main pages, 8 figures; 14 appendix pages, 25 ...
Tingwu Wang +9 more
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The Benefits of Model-Based Generalization in Reinforcement Learning
CoRR, 2022 Model-Based Reinforcement Learning (RL) is widely believed to have the potential to improve sample efficiency by allowing an agent to synthesize large amounts of imagined experience. Experience Replay (ER) can be considered a simple kind of model, which has proved effective at improving the stability and efficiency of deep RL.
Kenny Young +3 more
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UAV Anti-tank Policy Training Model Based on Curriculum Reinforcement Learning [PDF]
Jisuanji kexue, 2023 In the intelligent era,the battle for land battlefield expands from planar land control to vertical land control.UAV anti-tank operation plays a crucial role in the battle for land control in future intelligent war.Deep reinforcement learning method in ...
LIN Zeyang, LAI Jun, CHEN Xiliang, WANG Jun
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Value-Distributional Model-Based Reinforcement Learning
CoRR, 2023 Quantifying uncertainty about a policy's long-term performance is important to solve sequential decision-making tasks. We study the problem from a model-based Bayesian reinforcement learning perspective, where the goal is to learn the posterior distribution over value functions induced by parameter (epistemic) uncertainty of the Markov decision process.
Carlos E. Luis +4 more
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Decentralised demand response market model based on reinforcement learning
IET Smart Grid, 2020 A new decentralised demand response (DR) model relying on bi-directional communications is developed in this study. In this model, each user is considered as an agent that submits its bids according to the consumption urgency and a set of parameters ...
Miadreza Shafie-Khah +6 more
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Imagine to Ensure Safety in Hierarchical Reinforcement Learning
Machine Learning and Knowledge ExtractionThis work investigates the safe exploration problem in reinforcement learning, where an agent must maximize cumulative performance while simultaneously satisfying safety constraints.
Gregory Gorbov +2 more
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On the Use of Deep Reinforcement Learning for Visual Tracking: A Survey
IEEE Access, 2021 This paper aims at highlighting cutting-edge research results in the field of visual tracking by deep reinforcement learning. Deep reinforcement learning (DRL) is an emerging area combining recent progress in deep and reinforcement learning.
Giorgio Cruciata +2 more
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A survey on model-based reinforcement learning
Science China Information SciencesReinforcement learning (RL) solves sequential decision-making problems via a trial-and-error process interacting with the environment. While RL achieves outstanding success in playing complex video games that allow huge trial-and-error, making errors is always undesired in the real world.
Fan-Ming Luo +5 more
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Online Constrained Model-based Reinforcement Learning
CoRR, 2020 Conf.
Benjamin van Niekerk +2 more
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