Acceleration of Reinforcement Learning by Policy Evaluation Using Nonstationary Iterative Method

By K. Senda, Suguru Hattori, Toru Hishinuma, Takehisa Kohda. 2014

13 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Electrical and Electronic Engineering, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Yashesh D. Dhebar
2. Kalyanmoy Deb
3. Dimitar Filev
4. Ling Zhu
5. Subramanya P. Nageshrao
6. Yaochu Jin
7. Carlos Artemio Coello Coello
8. Ran Cheng

In Electrical and Electronic Engineering

1. Kalyanmoy Deb

Citations in the corpus

• Toward Interpretable-AI Policies Using Evolutionary Nonlinear Decision Trees for Discrete-Action Systems (2020, methodology): “Recent advances in nonlinear optimization approaches using evolutionary computation facilitate finding a hierarchical set of nonlinear control rules as a function of state variables using a computationally fast bilevel optimization procedure at each node of the proposed NLDT.”