Results 11 to 20 of about 7,404,725 (288)
Reinforcement Learning-based Spectrum Sharing for Cognitive Radio [PDF]
, 2011 This thesis investigates how distributed reinforcement learning-based resource assignment algorithms can be used to improve the performance of a cognitive radio system.
Jiang, Tao
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Safe Distributional Reinforcement Learning [PDF]
, 2022 Safety in reinforcement learning (RL) is a key property in both training and execution in many domains such as autonomous driving or finance. In this paper, we formalize it with a constrained RL formulation in the distributional RL setting. Our general model accepts various definitions of safety(e.g., bounds on expected performance, CVaR, variance, or ...
Jianyi Zhang, Paul Weng
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Improving Exploration in Reinforcement Learning through Domain Knowledge and Parameter Analysis [PDF]
, 2010 This thesis presents novel work on how to improve exploration in reinforcement learning using domain knowledge and knowledge-based approaches to reinforcement learning.
Grzes, Marek
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Learning to Recover for Safe Reinforcement Learning
CoRR, 2023 Safety controllers is widely used to achieve safe reinforcement learning. Most methods that apply a safety controller are using handcrafted safety constraints to construct the safety controller. However, when the environment dynamics are sophisticated, handcrafted safety constraints become unavailable.
Haoyu Wang, Xin Yuan, Qinqing Ren
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Multi-Task Policy Search for Robotics [PDF]
, 2014 24.02.14 KB. Ok to add accepted conference paper to spiral© 2014 IEEE.Learning policies that generalize across multiple tasks is an important and challenging research topic in reinforcement learning and robotics.
Englert, P +3 more
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, 2023 We summarize the IJCAI-23 Joint Workshop on Artificial Intelligence Safety and Safe Reinforcement Learning (AISafety-SafeRL2023)1, held at the 32nd International Joint Conference on Artificial Intelligence (IJCAI-23) on August 21-22, 2023 in Macau, China.
Pedroza, Gabriel +13 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 +3 more
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Virtuously Safe Reinforcement Learning
CoRR, 2018 We show that when a third party, the adversary, steps into the two-party setting (agent and operator) of safely interruptible reinforcement learning, a trade-off has to be made between the probability of following the optimal policy in the limit, and the probability of escaping a dangerous situation created by the adversary.
C. Henrik Åslund +3 more
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Reinforcement Learning by Guided Safe Exploration [PDF]
, 2023 Safety is critical to broadening the application of reinforcement learning (RL). Often, we train RL agents in a controlled environment, such as a laboratory, before deploying them in the real world. However, the real-world target task might be unknown prior to deployment.
Qisong Yang +4 more
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Comparing policy gradient and value function based reinforcement learning methods in simulated electrical power trade [PDF]
, 2012 In electrical power engineering, reinforcement learning algorithms can be used to model the strategies of electricity market participants. However, traditional value function based reinforcement learning algorithms suffer from convergence issues when ...
Burt, Graeme +3 more
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