Hill Climbing on Value Estimates for Search-control in Dyna
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.
Citation
Y. Pan,
H. Yao,
A. Farahmand,
M. White.
"Hill Climbing on Value Estimates for Search-control in Dyna". International Joint Conference on Artificial Intelligence (IJCAI), (ed: Sarit Kraus), pp 3209-3215, August 2019.
| Keywords: |
Machine Learning, Reinforcement Learning, Planning and Scheduling, Other approaches to planning Machine Learning, Deep Learning, Planning and Scheduling, Model-Based Reasoning |
| Category: |
In Conference |
| Web Links: |
DOI |
| |
IJCAI |
BibTeX
@incollection{Pan+al:IJCAI19,
author = {Yangchen Pan and Hengshuai Yao and Amir-Massoud Farahmand and
Martha White},
title = {Hill Climbing on Value Estimates for Search-control in Dyna},
Editor = {Sarit Kraus},
Pages = {3209-3215},
booktitle = {International Joint Conference on Artificial Intelligence
(IJCAI)},
year = 2019,
}Last Updated: February 24, 2020
Submitted by Sabina P 
