5G networks are designed for providing several different services as high speed Internet, low latency and M2M (Machine to Machine) communications. For enforcing such guaranteed services, slicing techniques are of essential importance to ensure isolation between resources allocated to each of these services, especially at the level of RAN (Radio Access Networks) and its time/frequency matrix. Given the scarcity of radio resources, this paper aims at proposing efficient radio resource allocation algorithms and mechanisms for 5G networks, avoiding resource wastes and enforcing slices isolation. The proposed solution highlights a new way of using reinforcement learning, and more specifically the Double DQN (Deep Q-Network) algorithm, based on a slotted approach for 5G resource allocations. The slotted use of Double DQN evaluation exhibits its benefits in terms of allocation performance and low latency.