/var/www/htdocs/pustaka-digital/lib/SearchEngine/SearchBiblioEngine.php:688 "Search Engine Debug 🔎 🪲"
Engine Type ⚙️: "SLiMS\SearchEngine\SearchBiblioEngine"
SQL ⚙️: array:2 [ "count" => "select count(sb.biblio_id) from search_biblio as sb where sb.opac_hide=0 and ((match (sb.author) against (:author in boolean mode)))" "query" => "select sb.biblio_id, sb.title, sb.author, sb.topic, sb.image, sb.isbn_issn, sb.publisher, sb.publish_place, sb.publish_year, sb.labels, sb.input_date, sb.edition, sb.collation, sb.series_title, sb.call_number from search_biblio as sb where sb.opac_hide=0 and ((match (sb.author) against (:author in boolean mode))) order by sb.last_update desc limit 10 offset 0" ]
Bind Value ⚒️: array:1 [ ":author" => "'+Dabney, Will'" ]
Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. Going beyond the common approach to reinforcement learning and expected values, it focuses on the total reward or return obtained as a consequence of an agent\'s choices—specifically, how this return behaves from a probabilistic perspective. In this first comprehensive guide to distributional r…