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Consumers' psychological constructs regarding hybrid meat products: A scoping review protocol. [PDF]
PLoS Onede Veld J, Pekdemir C, Ten Hoor G.
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Policy evaluation frameworks for rare diseases: a scoping review. [PDF]
BMC Health Serv ResÇakmak Barsbay M, Aydamak MY.
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Excess mortality in Mainland China after the end of the Zero COVID policy: A systematic review. [PDF]
Epidemiol InfectFung IC +10 more
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Growth Management and Constitutional Rights Part II: The States Search for a Growth Policy
, 1976 Fred P. Bosselman
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2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), 2011
In this paper we address the combination of batch reinforcement-learning (BRL) techniques with direct policy search (DPS) algorithms in the context of robot learning. Batch value-based algorithms (such as fitted Q-iteration) have been proved to outperform online ones in many complex applications, but they share the same difficulties in solving problems
MIGLIAVACCA, MARTINO +4 more
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In this paper we address the combination of batch reinforcement-learning (BRL) techniques with direct policy search (DPS) algorithms in the context of robot learning. Batch value-based algorithms (such as fitted Q-iteration) have been proved to outperform online ones in many complex applications, but they share the same difficulties in solving problems
MIGLIAVACCA, MARTINO +4 more
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2003
We investigate the problem of non-covariant behavior of policy gradient reinforcement learning algorithms. The policy gradient approach is amenable to analysis by information geometric methods. This leads us to propose a natural metric on controller parameterization that results from considering the manifold of probability distributions over paths ...
J. Andrew Bagnell, Schneider, Jeff
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We investigate the problem of non-covariant behavior of policy gradient reinforcement learning algorithms. The policy gradient approach is amenable to analysis by information geometric methods. This leads us to propose a natural metric on controller parameterization that results from considering the manifold of probability distributions over paths ...
J. Andrew Bagnell, Schneider, Jeff
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Nonconvex Policy Search Using Variational Inequalities
Neural Computation, 2017Policy search is a class of reinforcement learning algorithms for finding optimal policies in control problems with limited feedback. These methods have been shown to be successful in high-dimensional problems such as robotics control. Though successful, current methods can lead to unsafe policy parameters that potentially could damage hardware units.
Zhan, Yusen +2 more
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Policy Search by Dynamic Programming
2003We consider the policy search approach to reinforcement learning. We show that if a “baseline distribution” is given (indicating roughly how often we expect a good policy to visit each state), then we can derive a policy search algorithm that terminates in a finite number of steps, and for which we can provide non-trivial performance guarantees.
J. Andrew Bagnell +3 more
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