The　development　of　robust　and　agile　locomotion　skills　for　legged　robots　using　reinforcement　learning　is　challenging,　particularly　in　demanding　environments.　In　this　study,　we　propose　a　blind　locomotion　control　learning　framework　that　enables　fast　and　stable　walking　on　challenging　terrains.　First,　we　construct　an　asymmetric　terrain　feature　extraction　network　that　uses　a　multilayer　perceptron　to　effectively　infer　terrain　features　from　the　history　of　proprioceptive　states,　consisting　only　of　inertial　measurement　unit　and　joint　encoder　data.　Additionally,　our　asymmetric　actor-critic　framework　implicitly　infers　terrain　features,　thereby　enhancing　the　accuracy　of　terrain　representation.　Second,　we　introduce　a　foot　trajectory　generator　based　on　prior　gait　behaviors,　which　improves　the　gait　periodicity　and　provides　accurate　state　information　for　terrain　feature　inference.　Compared　to　state-of-the-art　methods,　our　approach　significantly　increases　the　learning　efficiency　by　26.0%　and　enhances　terrain　adaptation　by　5.0%.　It　also　achieved　a　more　periodic　gait,　with　the　state-command　tracking　error　reduced　by　38.5%　compared　with　advanced　methods.　The　success　rate　for　traversing　complex　terrains　was　similar　to　that　of　the　baseline　methods,　with　a　31.3%　increase　in　the　step　height　on　stair-like　terrains.　The　experimental　results　demonstrate　that　the　proposed　method　enables　fast　and　stable　walking　on　challenging　terrains.