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Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learning
[article]
2022
arXiv
pre-print
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In lifelong learning, an agent learns throughout its entire life without resets, in a constantly changing environment, as we humans do. ...
Consequently, lifelong learning comes with a plethora of research problems such as continual domain shifts, which result in non-stationary rewards and environment dynamics. ...
We investigate the effect of three additional exploration methods (RND, RIDE and NovelD) and compare them to the PPO baseline. We use α = 0.85 for all experiments. ...
arXiv:2207.05742v1
fatcat:qizjj6dqhnaahirpoktb4yzbiq
Exploration in Deep Reinforcement Learning: A Comprehensive Survey
[article]
2022
arXiv
pre-print
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In addition to algorithmic analysis, we provide a comprehensive and unified empirical comparison of different exploration methods for DRL on a set of commonly used benchmarks. ...
In this paper, we conduct a comprehensive survey on existing exploration methods for both single-agent and multi-agent RL. ...
[110] propose a new criterion, called NovelD, which assigns intrinsic rewards to states at the boundary between already explored and unexplored regions. ...
arXiv:2109.06668v4
fatcat:6hmuo66i6rbw3olsy4sbydoryq
Extensive, Nonrandom Diversity of Excision Footprints Generated byDs-Like TransposonAscot-1Suggests New Parallels with V(D)J Recombination
1998
Molecular and Cellular Biology
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A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
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We show that this system, which produces many phenotypically and genetically distinct derivatives, results from the excision of a novelDs-like transposon,Ascot-1, from the spore color geneb2. ...
Products varied in their frequency of occurrence over 4 orders of magnitude, yet most showed small palindromic nucleotide additions. ...
simple end-joining reaction. ...
doi:10.1128/mcb.18.7.4337
pmid:9632817
pmcid:PMC109017
fatcat:w72rwhyumnh3zn4x7bzt2slyqa