A contextual quality-diversity evolutionary RL controller matches a single soft actor-critic policy on annual energy (about 3.4% savings over ASHRAE Guideline 36 in simulation) but with 272 times lower seed-to-seed variance in chiller starts.
Combining Evolution and Deep Reinforcement Learning for Policy Search: a Survey
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abstract
Deep neuroevolution and deep Reinforcement Learning have received a lot of attention in the last years. Some works have compared them, highlighting theirs pros and cons, but an emerging trend consists in combining them so as to benefit from the best of both worlds. In this paper, we provide a survey of this emerging trend by organizing the literature into related groups of works and casting all the existing combinations in each group into a generic framework. We systematically cover all easily available papers irrespective of their publication status, focusing on the combination mechanisms rather than on the experimental results. In total, we cover 45 algorithms more recent than 2017. We hope this effort will favor the growth of the domain by facilitating the understanding of the relationships between the methods, leading to deeper analyses, outlining missing useful comparisons and suggesting new combinations of mechanisms.
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cs.LG 1years
2026 1verdicts
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Contextual Quality-Diversity Evolutionary Reinforcement Learning for HVAC Control in Tropical Commercial Buildings
A contextual quality-diversity evolutionary RL controller matches a single soft actor-critic policy on annual energy (about 3.4% savings over ASHRAE Guideline 36 in simulation) but with 272 times lower seed-to-seed variance in chiller starts.