Nanopublication

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https://w3id.org/np/RAbv_E_U02qVYAHDisjKEUhi7qQYFsjhGqL24QEbWRP78
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Content

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sub:assertion {
  <https://doi.org/10.1145/3712256.3726452> a <https://w3id.org/fair/ff/terms/article>,
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    dct:creator orcid:0000-0001-9487-5622;
    dct:publisher <https://ror.org/021nxhr62>;
    dct:subject <http://edamontology.org/topic_3316>;
    rdfs:comment "Ant Colony Optimization (ACO) has served as a widely-utilized metaheuristic algorithm for decades for solving combinatorial optimization problems. Since its initial construction, ACO has seen a wide variety of modifications and connections to Reinforcement Learning (RL). Substantial parallels can be seen as early as 1995 with Ant-Q's relationship with Q-learning, through 2022 with ADACO's connection with Policy Gradient. In this work, we describe ACO, more specifically the Stochastic Gradient Descent ACO algorithm (ACOSGD), explicitly as an off-policy Policy Gradient (PG) method. We also incorporate experience replay into several ACO algorithm variants, including AS, MaxMin-ACO, ACOSGD, ADACO, and our two policy gradient-based versions: PGACO and PPOACO, drawing the connection to elitist ACO strategies. We show that our implementation of PG in ACO with experience replay and a baselined reward update strategy applied to eight TSP problems of varying sizes performs competitively with both fundamental ACO and SGD-based ACO versions. We also show that the replay buffer seems to unilaterally improve the performance of ACO algorithms through an ablation study";
    rdfs:label "Ant Colony Optimization with Policy Gradients and Replay";
    <https://w3id.org/fdof/ontology#hasMetadata> this:;
    <https://www.w3.org/ns/dcat#contactPoint> "john.sheppard@montana.edu";
    <https://www.w3.org/ns/dcat#endDate> "July 13 2025";
    <https://www.w3.org/ns/dcat#startDate> "2024" .
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  orcid:0009-0008-8411-2742 foaf:name "Emily Regalado" .
  
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    dct:license <https://creativecommons.org/licenses/by/4.0/>;
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