Grammatical Evolution Guided by Reinforcement is an extension of Grammatical Evolution that tries to improve the evolutionary process adding a learning process for all the individuals in the population. With this aim, each individual is given a chance to learn through a reinforcement learning mechanism during its lifetime. The learning process is completed with a Lamarckian mechanism in which an original genotype is replaced by the best learnt genotype for the individual. In a way, Grammatical Evolution Guided by Reinforcement shares an important feature with other hybrid algorithms, i.e. global search in the evolutionary process combined with local search in the learning process. In this paper the role of the Lamarck Hypothesis is reviewed and a solution inspired only in the Baldwin effect is included as well. Besides, different techniques about the trade-off between exploitation and exploration in the reinforcement learning step followed by Grammatical Evolution Guided by Reinforcement are studied. In order to evaluate the results, the system is applied on two different domains: a simple autonomous navigation problem in a simulated Kephera robot and a typical Boolean function problem.