Results 51 to 60 of about 8,751,520 (309)
Evolutionary Reinforcement Learning: A Systematic Review and Future Directions
MathematicsIn response to the limitations of reinforcement learning and Evolutionary Algorithms (EAs) in complex problem-solving, Evolutionary Reinforcement Learning (EvoRL) has emerged as a synergistic solution.
Yuanguo Lin +6 more
doaj +1 more source
Search Design Policy, Digital Disruption and Competition Law
Market and Competition Law Review, 2017 It is debatable whether traditional competition law tools and remedies are able to deal with the digital disruption and whether it is desirable to adjust or even replace categories that have proven to be mainly suited to tackle anticompetitive conducts ...
Valeria Falce, Massimiliano Granieri
doaj +1 more source
The Impact of Socialist Imprinting and Search for Knowledge on Resource Change: An Empirical Study of Firms in Lithuania [PDF]
, 2002 In this paper we examine how firms change their resources in response to exogenous shocks in their business environment. Building on core ideas from the literatures on organizational imprinting and firm resources, we suggest that founding conditions ...
Kale, Prashant, Kriauciunas, Aldas
core +1 more source
OPTIMAL STABILIZATION POLICY WITH SEARCH EXTERNALITIES [PDF]
Macroeconomic Dynamics, 2013 We study optimal monetary stabilization policy in a DSGE model with microfounded money demand. A search externality creates “congestion,” which causes aggregate output to be inefficient. Because of the informational frictions that give rise to money, households are unable to insure themselves perfectly against aggregate shocks.
Berentsen, Aleksander +1 more
openaire +2 more sources
Curiosity Creates Diversity in Policy Search
ACM Transactions on Evolutionary Learning and Optimization, 2023 When searching for policies, reward-sparse environments often lack sufficient information about which behaviors to improve upon or avoid. In such environments, the policy search process is bound to blindly search for reward-yielding transitions and no early reward can bias this search in one direction or another.
Paul-Antoine Le Tolguenec +3 more
openaire +2 more sources
Adaptive Control with Approximated Policy Search Approach
ITB Journal of Engineering Science, 2010 Most of existing adaptive control schemes are designed to minimize error between plant state and goal state despite the fact that executing actions that are predicted to result in smaller errors only can mislead to non-goal states. We develop an adaptive
Agus Naba
doaj +1 more source
Policy Search Reinforcement Learning Method in Latent Space [PDF]
Jisuanji kexue yu tansuoPolicy search is an efficient learning method in the field of deep reinforcement learning (DRL), which is capable of solving large-scale problems with continuous state and action spaces and widely used in real-world problems. However, such method usually
ZHAO Tingting, WANG Ying, SUN Wei, CHEN Yarui, WANG Yuan, YANG Jucheng
doaj +1 more source
Ibom Medical Journal, 2020 Context: The public health importance of cancers in Nigeria is emerging. Robust cancer control policies are needed at all levels of government, especially the state.
Eguzo K +5 more
doaj +1 more source
Antibiotics, 2022 The Dutch ‘search and destroy’ policy consists of screening patients with an increased risk of methicillin-resistant Staphylococcus aureus (MRSA) carriership and subsequent decolonization treatment when carriership is found.
Annette C. Westgeest +7 more
doaj +1 more source
Reinforcement learning of motor skills using Policy Search and human corrective advice
Int. J. Robotics Res., 2019 Robot learning problems are limited by physical constraints, which make learning successful policies for complex motor skills on real systems unfeasible.
C. Celemin +4 more
semanticscholar +1 more source