Network　slicing　enables　the　multiplexing　of　independent　logical　networks　on　the　same　physical　network　infrastructure　to　provide　different　network　services　for　different　applications.　The　resource　allocation　problem　involved　in　network　slicing　is　typically　a　decision-making　problem,　falling　within　the　scope　of　reinforcement　learning.　The　advantage　of　adapting　to　dynamic　wireless　environments　makes　reinforcement　learning　a　good　candidate　for　problem　solving.　In　this　paper,　to　tackle　the　constrained　mixed　integer　nonlinear　programming　problem　in　network　slicing,　we　propose　an　augmented　Lagrangian-based　soft　actor-critic　(AL-SAC)　algorithm.　In　this　algorithm,　a　hierarchical　action　selection　network　is　designed　to　handle　the　hybrid　action　space.　More　importantly,　inspired　by　the　augmented　Lagrangian　method,　both　neural　networks　for　Lagrange　multipliers　and　a　penalty　item　are　introduced　to　deal　with　the　constraints.　Experiment　results　show　that　the　proposed　AL-SAC　algorithm　can　strictly　satisfy　the　constraints,　and　achieve　better　performance　than　other　benchmark　algorithms.