Results 21 to 30 of about 37,169,246 (294)
Modeling Survival in model-based Reinforcement Learning [PDF]
2020 Second International Conference on Transdisciplinary AI (TransAI), 2020 Although recent model-free reinforcement learning algorithms have been shown to be capable of mastering complicated decision-making tasks, the sample complexity of these methods has remained a hurdle to utilizing them in many real-world applications. In this regard, model-based reinforcement learning proposes some remedies. Yet, inherently, model-based
Saeed Moazami, Peggy Doerschuk
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Continual Model-Based Reinforcement Learning with Hypernetworks [PDF]
2021 IEEE International Conference on Robotics and Automation (ICRA), 2021 Effective planning in model-based reinforcement learning (MBRL) and model-predictive control (MPC) relies on the accuracy of the learned dynamics model. In many instances of MBRL and MPC, this model is assumed to be stationary and is periodically re-trained from scratch on state transition experience collected from the beginning of environment ...
Yizhou Huang +3 more
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Parallel model-based and model-free reinforcement learning for card sorting performance
Scientific Reports, 2020 The Wisconsin Card Sorting Test (WCST) is considered a gold standard for the assessment of cognitive flexibility. On the WCST, repeating a sorting category following negative feedback is typically treated as indicating reduced cognitive flexibility ...
Alexander Steinke +2 more
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Model-Based Reinforcement Learning via Stochastic Hybrid Models
IEEE Open Journal of Control Systems, 2023 Optimal control of general nonlinear systems is a central challenge in automation. Enabled by powerful function approximators, data-driven approaches to control have recently successfully tackled challenging applications.
Hany Abdulsamad, Jan Peters
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Model-based Lookahead Reinforcement Learning
CoRR, 2019 Model-based Reinforcement Learning (MBRL) allows data-efficient learning which is required in real world applications such as robotics. However, despite the impressive data-efficiency, MBRL does not achieve the final performance of state-of-the-art Model-free Reinforcement Learning (MFRL) methods. We leverage the strengths of both realms and propose an
Zhang-Wei Hong +2 more
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Reinforcement learning in populations of spiking neurons [PDF]
, 2008 Population coding is widely regarded as a key mechanism for achieving reliable behavioral responses in the face of neuronal variability. But in standard reinforcement learning a flip-side becomes apparent.
Urbanczik, R +6 more
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Tongxin xuebao, 2023 To solve the problem that intelligent devices equipped with deep reinforcement learning agents lack effective security data sharing mechanisms in the intelligent Internet of things, a general federated reinforcement learning (GenFedRL) framework was ...
Biao JIN +4 more
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Journal of Robotics, 2022 Deep reinforcement learning is the technology of artificial neural networks in the field of decision-making and control. The traditional model-free reinforcement learning algorithm requires a large amount of environment interactive data to iterate the ...
Guoqing Ma +3 more
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MOReL : Model-Based Offline Reinforcement Learning
CoRR, 2020 First two authors contributed equally. Published at NeurIPS 2020. After publication at NeurIPS 2020, (1) D4RL benchmark results have been added; (2) hyper-parameter ablation studies have been added; (3) scope of Lemma 3 has been ...
Rahul Kidambi +3 more
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Plug and Play, Model-Based Reinforcement Learning
CoRR, 2021 Sample-efficient generalisation of reinforcement learning approaches have always been a challenge, especially, for complex scenes with many components. In this work, we introduce Plug and Play Markov Decision Processes, an object-based representation that allows zero-shot integration of new objects from known object classes.
Majid Abdolshah +5 more
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