We discuss the online optimization of redundant copies of computing tasks. The method helps to overcome the limitations of cloud and edge computing methods for mobile applications where the shared use of mixed and distributed processors often yields unpredictable execution times. While the aim is to obtain the earliest response (discarding the rest), without careful control of the task replication process, the redundant executions may lead to excessive processor contention leading to undesired effects. The current state of practice assumes the use of homogeneous processors, heuristics, and perfect knowledge of the future task runtimes, which limit the application scope of the idea. Through reinforcement learning, the proposed method discovers autonomously how to optimally select for each new request both the number of replicas and their processor assignment. The method can operate effectively without full knowledge of system features and regardless of the changing system state. An extensive simulation study using different scenarios confirms the performance of this proposal and offers a quantitative insight into its advantages and limitations. Diverse mobile applications prospectively benefit from this approach, as computer and communication networks become larger and more diverse, and mobile applications require increasingly higher reliability and lower latency.