This paper describes a learning system based on resilience indicators. It proposes a hybrid learning system to estimate Human–Machine System performance when facing unprecedented situations. Collected data from various criteria are compared with data estimated using the local and the global resilience indicators, to give both instantaneous and over-time Human–Machine System states. The learning system can be composed of two different, separate reinforcement functions; the first allowing reinforcement of its own system knowledge and the second allowing reinforcement of its estimation function. When used together in a hybrid approach, the resilience indicator estimation should be improved. The learning system is then applied in a simulated air transport context and the impact of each reinforcement function on resilience indicator estimation is assessed. The hypothesis on performance of hybrid reinforcement learning is confirmed and it provides better results than those obtained by the knowledge based reinforcement or the estimation based reinforcement alone.