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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
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. ...  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
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