<prompt> Hlahisa kakaretso ea mantsoe a 100 ea pampiri e latelang e hlalosang likopo tsa eona tsa mantlha le litsebetso.Pampiri ena e fana ka algorithm e matla ea ho ithuta ea ho ithuta ho bapala papali ea komporo Doom. Algorithm e sisintsoeng ke tsebetsong ea leano le tebileng la deterministic le sebaka sa tsebetso, ho itšetlehile ka convolutional neural network CNN ho etsa mapa oa liforeimi tsa papali ho ea ho ketso e lebeletsoeng. Liteko tse entsoeng ka mefuta e meli ea papali ea Doom li bontsitse hore algorithm e sisintsoeng e feta tharollo ea mantlha ho latela ho nepahala, e bontsang tsebetso e phahameng ka li-CNN tse tebileng. Patlisiso ena e bontsa monyetla oa ho ithuta ka matlafatso e tebileng bakeng sa papali ea AI. Ka thuso ea mekhoa e tebileng ea ho ithuta, mohlala oa rona o khona ho utloisisa likhopolo tse sa utloahaleng le ho etsa mesebetsi e rarahaneng ea ho tsamaea, ho netefatsa ho nepahala ho phahameng ha ho bapisoa le mokhoa oa setso. Ho hlahloba tsebetso ea li-algorithms tse ling tse matlafatsang ho ithuta lipapaling tsa Doom ho ka ntlafatsa ho nepahala ha kakaretso. Lisebelisoa tsa mokhoa o reriloeng li ka fetela ka nģane ho AI ea papali ho ea ho tsebetso ea robotic navigation le mesebetsi ea taolo, tsebetso ea litšoantšo, le ho etsa liqeto. Ka ho amohela mekhoa e rarahaneng ea ho matlafatsa thuto, joalo ka ho ithuta ka ho etsisa, ho ithuta ka maemo a phahameng le ho ithuta ka meta, mokhoa o reriloeng o ka sebelisoa mesebetsing e fapaneng e tsamaisoang ke AI, ho kenyeletsoa liroboto le tlhokomelo ea bophelo bo botle, ntoa le ho khanna ka boithaopo.  <response>: Instruction in English: Generate a 100-word synopsis of the following paper describing its main claims and applications. . Response in English: This paper presents the application of a deep reinforcement learning algorithm for playing the game Doom. The algorithm is a reimplementation of deep deterministic policy gradient that uses convolutional neural networks to map game frames to the expected action. Experiments showed the algorithm outperforms the baseline solution by achieving higher accuracy with deeper CNNs. The approach is applicable to other domains such as robotic navigation, image processing, and decision-making. Further improvement can be obtained by embracing other complex reinforcement learning techniques, expanding the methods application to areas such as robotics, healthcare, combat, and autonomous driving. . Response in Sesotho: Pampiri ena e fana ka tsebeliso ea algorithm e matla ea ho ithuta ea ho bapala papali Doom. Algorithm ke tsebetsong ea leano le tebileng la deterministic le sebelisang marang-rang a convolutional neural ho etsa mapa oa liforeimi tsa lipapali ho latela ketso e lebelletsoeng. Liteko li bontsitse algorithm e feta tharollo ea mantlha ka ho fumana ho nepahala ho phahameng ka li-CNN tse tebileng. Mokhoa ona o sebetsa libakeng tse ling tse kang ho tsamaisa liroboto, ho sebetsana le litšoantšo, le ho etsa liqeto. Ntlafatso e eketsehileng e ka fumanoa ka ho amohela mekhoa e meng e rarahaneng ea ho matlafatsa ho ithuta, ho holisa tsebeliso ea mokhoa ona libakeng tse kang liroboto, tlhokomelo ea bophelo bo botle, ntoa le ho khanna motho a le mong. 