To　solve　the　problem　of　autonomous　steering　of　autonomous　vehicles,　most　researches　are　mainly　based　on　the　model　predictive　control(MPC)　strategy,　while　the　traditional　MPC　strategy　requires　an　accurate　mathematical　model　of　the　controlled　object　and　a　lot　of　real-time　control　calculations.　To　this　end,　a　steering　control　strategy　based　on　deep　Q-Learning　neural　network(DQN)　reinforcement　learning　is　proposed,　which　enables　autonomous　vehicles　to　track　paths　accurately　and　effectively,　and　improves　the　accuracy　and　stability　of　path　tracking.　The　strategy　is　based　on　DQN　reinforcement　learning　to　train　the　agent　by　selecting　an　appropriate　learning　rate,　so　that　the　trained　agent　can　adaptively　obtain　the　best　front　wheel　turning　angle　according　to　different　road　conditions　and　vehicle　speeds.　The　simulation　comparison　results　show　that　compared　with　the　unconstrained　linear　quadratic　regulator(LQR)　control　strategy,　the　cumulative　absolute　lateral　position　deviation　and　cumulative　absolute　yaw　angle　deviation　of　the　control　strategy　based　on　DQN　reinforcement　learning　have　increased　significantly.　But　it　is　also　within　an　acceptable　range,　which　can　effectively　improve　the　accuracy　of　path　tracking.　The　final　real　vehicle　test　results　also　show　the　effectiveness　of　the　proposed　control　strategy.　©　2023　Editorial　Office　of　Chinese　Journal　of　Mechanical　Engineering.　All　rights　reserved.