Quadrupedal　robots　have　more　powerful　locomotion　capabilities　compared　to　other　types　of　mobile　robots.　However,　the　complexity　of　their　controllers　and　the　need　to　adapt　to　a　wide　variety　of　terrains　poses　a　huge　difficulty　in　the　design　of　the　controllers.　Recently,　deep　reinforcement　learning　(DRL)　has　been　widely　used　in　quadruped　robot　controller　design　but　still　relies　on　complex　and　reliable　sensing　frameworks.　In　this　paper,　we　propose　a　motion　controller　training　framework.　The　framework　includes　an　Environmental　Feature　Assessment　Network　(EFANet)　to　guide　the　action　output　of　the　policy　network,　and　an　asymmetric　actor-critic　structure　to　help　the　policy　network　infer　terrain　features.　We　also　introduce　a　strategic　trajectory　generator　into　our　framework　to　prevent　the　robot　from　generating　abnormal　gaits.　The　proposed　framework　for　learning　quadrupedal　locomotion　allows　quadrupedal　robots　to　traverse　challenging　terrains　with　limited　sensors.　Finally,　the　proposed　approach　is　tested　and　evaluated　in　a　simulation　environment　using　the　A1　quadruped　robot　model.　©　2023　IEEE.