Redirected　Walking　(RDW)　technique　allows　users　to　walk　indefinitely　in　a　limited　physical　space　while　keeping　the　feeling　of　real　walking.　Developers　use　RDW　controllers　to　manage　RDW　techniques　based　on　physical　and　virtual　environment　information.　However,　traditional　RDW　controllers　still　suffer　from　many　problems.　For　example,　generalized　controllers　are　less　optimized,　and　scripted　controllers　are　difficult　to　handle　unexpected　movements.　Based　on　reinforcement　learning,　we　present　a　novel　RDW　controller　that　allows　the　user　to　explore　complex　and　large　virtual　environments　while　minimizing　the　number　of　collisions　with　obstacles　in　the　physical　environments.　Our　RDW　controller　directly　prescribes　the　translation,　rotation,　and　curvature　gains　by　analyzing　real-time　information　of　the　physical　environment.　The　simulation-based　experiments　show　that　our　controller　significantly　reduces　the　number　of　resets　caused　by　collisions　between　user　and　obstacles　of　physical　spaces　compared　to　steer-to-center(S2C)　and　current　state-of-the-art　controllers　using　reinforcement　learning.　©　2023　SPIE.