A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
Instance Weighted Incremental Evolution Strategies for Reinforcement Learning in Dynamic Environments
[article]
2020
arXiv
pre-print
Evolution strategies (ES), as a family of black-box optimization algorithms, recently emerge as a scalable alternative to reinforcement learning (RL) approaches such as Q-learning or policy gradient, and are much faster when many central processing units (CPUs) are available due to better parallelization. In this paper, we propose a systematic incremental learning method for ES in dynamic environments. The goal is to adjust previously learned policy to a new one incrementally whenever the
arXiv:2010.04605v1
fatcat:pdrqxoruvfge3lrimj7ruvnyme