Evolutionary Algorithms have been applied in robotics over the last quarter of a century to simultaneously evolve robot body morphology and controller. However, the literature shows that in this area, one is still unable to generate robots that perform better than conventional manual designs, even for simple tasks. It is noted that the main hindrance to satisfactory evolution is poor controller generated as a result of the simultaneous variation of the morphology and controller. As the controller is a result of pure evolution, it is not given a chance to improve before fitness is calculated, which is the equivalent of reproducing immediately after birth in biological evolution. Therefore, to improve co-evolution and to bring artificial robot evolution a step closer to biological evolution, this paper introduces Reinforced Co-evolution Algorithm (ReCoAl), which is a hybrid of an Evolutionary and a Reinforcement Learning algorithm. It combines the evolutionary and learning processes found in nature to co-evolve robot morphology and controller. ReCoAl works by allowing a direct policy gradient based RL algorithm to improve the controller of an evolved robot to better utilise the available morphological resources before fitness evaluation. The ReCoAl is tested for evolving mobile robots to perform navigation and obstacle avoidance. The findings indicate that the controller learning process has both positive and negative effects on the progress of evolution, similar to observations in evolutionary biology. It is also shown how, depending on the effectiveness of the learning algorithm, the evolver generates robots with similar fitness in different generations.