Recent　years　have　witnessed　continuous　optimization　and　innovation　of　reinforcement　learning　algorithms.　Games,　as　a　key　application　paradigm,　have　been　widely　employed　to　develop　superior　reinforcement　learning　models.　Different　game　environments　present　diverse　challenges　to　reinforcement　learning　agents;　however,　mainstream　gaming　paradigms　have　not　yet　specifically　addressed　issues　such　as　variable　action　spaces　and　varying　tasks.　In　this　regard,　this　paper　introduces　a　two-player　adversarial　game　with　a　configurable　player　action　space.　The　game　allows　for　the　diversification　of　task　challenges　by　configuring　opponent　strategies.　Additionally,　we　propose　a　reinforcement　learning　method　to　facilitate　the　decision-making　of　the　AI　player　(the　agent)　in　the　game.　The　inclusion　of　an　action　masking　algorithm　enables　effective　handling　of　variable　action　space　issues.　Experimental　results　indicate　that　the　decision-making　behavior　of　the　agent　adjusts　with　changes　in　opponent　behavior　and　continuously　improves　with　policy　updates.　The　trained　agent　exhibits　impressive　performance　in　this　game,　it　shows　that　the　proposed　method　could　serve　as　a　baseline　for　the　decision-making　in　the　novel　game,　and　a　robust　foundation　for　further　research　and　applications　is　provided.　©　2024　IEEE.