We will discuss the dynamics of stochastic learning strategies for the 'Kolkata Paise Restaurant' problem, where N agents choose among N equally priced but differently ranked restaurants every evening such that each agent tries get to dinner in the best restaurant (each serving only one customer and the rest arriving there going without dinner that evening). Informations regarding the restaurant ques in the previous nights are available to everybody and each agent employs strategies in parallel to choose the respective restaurant every evening. We consider the learning strategies to be similar for all the agents and assume that each follow the same probabilistic or stochastic strategy dependent on the information of the past successes in the game. We show that some 'naive' strategies lead to much better utilization of the services than some relatively 'smarter' strategies. A "dictated " solution to the problem is much more efficient and quicker than the above- mentioned ("democratically arrived") solutions. Both numerical and analytical results will be discussed. Refs: 1. A. S. Chakrabarti, B. K. Chakrabarti, A. Chatterjee & M. Mitra, Kolkata Paise Restaurant Problem & Resource Utilisation, Physica A 388 (2009) 2420 2. A. Ghosh & B. K. Chakrabarti, Kolkata Paise Restaurant Problem, http://demonstrations.wolfram.com/KolkataPaiseRestaurantKPRProblem 3. A. Ghosh, A. Chatterjee, M. Mitra & B. K. Chakrabarti , Statistics of Kolkata Paise Restaurant Problem, New J. Phys. 12 (2010; in press)