Deep　reinforcement　learning　at　the　same　time　combines　the　perception　of　deep　learning　and　the　decision-making　of　reinforcement　learning,　is　currently　a　hot　research　topic　in　the　field　of　artificial　intelligence.　Multi-agent　deep　reinforcement　learning　applies　the　idea　and　algorithm　of　deep　reinforcement　learning　to　the　learning　and　control　of　multi-agent　system,　which　is　an　important　method　to　develop　multi-agent　system　with　swarm　agent.　Multi-agent　deep　deterministic　policy　gradient(MADDPG)　is　the　most　popular　model-free　multi-agent　reinforcement　learning　algorithm.　To　solve　the　problem　of　low　learning　and　training　efficiency　and　slow　convergence　speed　of　MADDPG　due　to　the　deterministic　single　action　output　of　policy　network,　this　paper　combines　the　maximum　reinforcement　learning　soft　actor　-critic　algorithm　to　make　each　agent＇s　policy　network　output　action　with　a　random　strategy　and　propose　a　multi-agent　deep　reinforcement　learning　algorithm　MASAC　based　on　maximum　entropy.　The　experimental　results　show　that　the　training　speed　of　MASAC　is　better　than　that　of　MADDPG.　At　the　same　time,　the　learning　agent　has　good　performance,　stable　performance　and　strong　anti-interference　ability.　©　2021　IEEE.