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This paper introduces the Reinforced Genetic Programming (RGP) system, which enhances standard tree-based genetic programming (GP) with reinforcement learning (RL). RGP adds a new element to the GP function set: monitored action-selection points that provide hooks to a reinforcement-learning system. Using strong typing, RGP can restrict these choice points to leaf nodes, thereby turning GP trees into classify-and-act procedures. Then, environmental reinforcements channeled back through the choice points provide the basis for both lifetime learning and general GP fitness assessment. This paves the way for evolutionary acceleration via both Baldwinian and Lamarckian mechanisms. In addition, the hybrid hints of potential improvements to RL by exploiting evolution to design proper abstraction spaces, via the problem-state classifications of the internal tree nodes. This paper details the basic mechanisms of RGP and demonstrates its application on a series of static and dynamic maze-search problems.