Search Control Algorithm Based on Random Step Size Hill-Climbing Method for Adaptive PMD Compensation

2009 ◽  
Vol E92.B (8) ◽  
pp. 2584-2590 ◽  
Author(s):  
Ken TANIZAWA ◽  
Akira HIROSE
Keyword(s):  
Control Algorithm ◽  
Hill Climbing ◽  
Step Size ◽  
Pmd Compensation ◽  
Random Step ◽  
Search Control

scholarly journals Hill Climbing on Value Estimates for Search-control in Dyna

2019 ◽  
Author(s):  
Yangchen Pan ◽  
Hengshuai Yao ◽  
Amir-massoud Farahmand ◽  
Martha White
Keyword(s):  
Value Function ◽  
Gradient Algorithm ◽  
Hill Climbing ◽  
Value Functions ◽  
Current Estimate ◽  
Sampling Distributions ◽  
Current Value ◽  
Empirical Demonstration ◽  
Search Control ◽  
The Value Function

Dyna is an architecture for model based reinforcement learning (RL), where simulated experience from a model is used to update policies or value functions. A key component of Dyna is search control, the mechanism to generate the state and action from which the agent queries the model, which remains largely unexplored. In this work, we propose to generate such states by using the trajectory obtained from Hill Climbing (HC) the current estimate of the value function. This has the effect of propagating value from high value regions and of preemptively updating value estimates of the regions that the agent is likely to visit next. We derive a noisy projected natural gradient algorithm for hill climbing, and highlight a connection to Langevin dynamics. We provide an empirical demonstration on four classical domains that our algorithm, HC Dyna, can obtain significant sample efficiency improvements. We study the properties of different sampling distributions for search control, and find that there appears to be a benefit specifically from using the samples generated by climbing on current value estimates from low value to high value region.

Low-Complexity Time-Domain DBP Based on Random Step-Size and Partitioned Quantization

2018 ◽  
Vol 36 (14) ◽  
pp. 2888-2895 ◽  
Author(s):  
Celestino S. Martins ◽  
Luca Bertignono ◽  
Antonino Nespola ◽  
Andrea Carena ◽  
Fernando P. Guiomar ◽  
...  
Keyword(s):  
Time Domain ◽  
Low Complexity ◽  
Step Size ◽  
Random Step
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