Sample|Efficient Evolutionary Function Approximation ...

Sample-Efficient Evolutionary Function Approximation for ...

evolutionary function approximation sample-efficient. In domains with large or continuous state spaces, the value function cannot be represented in a table.

Sample-Efficient Evolutionary Function Approximation for ...

A recently developed approach called evolutionary function approximation uses evolutionary computation to automate the search for effective ...

Sample-Efficient Evolutionary Function Approximation ... - CiteSeerX

evolutionary function approximation sample-efficient. In domains with large or continuous state spaces, the value function cannot be represented in a table.

Sample-Efficient Evolutionary Function Approximation | SpringerLink

As mentioned in Section 4.3, evolutionary function approximation suffers from one important disadvantage: high sample complexity.

[PDF] Sample-Efficient Evolutionary Function Approximation for ...

This work presents an enhancement to evolutionary function approximation that makes it much more sample-efficient by exploiting the off-policy nature of ...

Sample-Efficient Evolutionary Function Approximation - SpringerLink

As mentioned in Section 4.3, evolutionary function approximation suffers from one important disadvantage: high sample complexity.

Sample-efficient evolutionary function approximation for ...

A recently developed approach called evolutionary function approximation uses evolutionary computation to automate the search for effective ...

Evolutionary Function Approximation for Reinforcement Learning

This paper investigates evolutionary function approximation, a novel approach to automatically selecting function approximator representations that enable ...

Evolutionary Function Approximation for Reinforcement Learning

function approximator representations that enable efficient individual learning. Thus, this method evolves individuals that are better able to learn. This ...

Sample-Efficient Evolutionary Function Approximation for ... - dblp

Bibliographic details on Sample-Efficient Evolutionary Function Approximation for Reinforcement Learning.

Evolutionary Function Approximation for Reinforcement Learning

This paper investigates evolutionary function approximation, a novel approach to automatically selecting function approximator representations that enable ...

An efficient evolutionary algorithm for accurate polygonal ...

It has been demonstrated that the proposed EEA outperforms the existing GA-based method, especially in solving large polygonal approximation problems in terms ...

Evolutionary Function Approximation - Peter Stone - Academia.edu

In practice, the success of function approximators depends on the ability of the human designer to select an appropriate representation for the value function.

Evolutionary Function Approximation for Reinforcement Learning

work, it has very high sample complexity. In ongoing research, we are investigating ways of making. NEAT+Q more sample-efficient. For example, preliminary ...

An Efficient Evolutionary Algorithm for Minimum Cost Submodular ...

first polynomial-time evolutionary approximation algorithm for Minimum Cost ... Maximizing submodular functions under ma- troid constraints by multi-objective ...

(PDF) Evolutionary value function approximation | Mohsen ...

... function. download Download PDF View PDFchevron_right. Free PDF. Sample-efficient evolutionary function approximation for reinforcement learning Cover Page.

An Efficient Evolutionary Algorithm for Subset Selection with General ...

Iyer and Bilmes (2013) proposed several algorithms with bounded approximation guarantees by using suitable surro- gate functions for f and c to optimize over.

Evolutionary Function Approximation for Reinforcement Learning

evolutionary function approximation, a novel approach to automatically selecting function approximator representations that enable efficient in- dividual ...

Evolutionary Reinforcement Learning: A Survey - arXiv

Sample-efficient evolutionary function approximation for reinforcement learning. Proc Natl Conf. Artif Intell. 2006;21(1):518. 104. Whiteson S, Taylor ME ...

An Efficient Evolutionary Algorithm for Subset Selection with General ...

Iyer and Bilmes (2013) proposed several algorithms with bounded approximation guarantees by using suitable surro- gate functions for f and c to optimize over.