—Human inactivity is handled by adapting the behavioral changes of the users. Human inactivity refers to as unpredictable workload of a complex system that is caused by increments of amount in power consumption and it can be handled automatically without the need to set a fixed time for changing the computer state. This is happens due to lack of knowledge in a software system and the software self-adaptation is one approach in dealing with this source of uncertainty. This paper observes human inactivity and Power management policy through the application of reinforcement learning approach in the computer usage and finds that computer power usage can be reduced if the idle period can be intelligently sensed from the user activities. This study introduces Control, Learn and Knowledge model that adapts the Monitor, Analyze, Planning, Execute control loop integrates with Q Learning algorithm to learn human inactivity period to minimize the computer power consumption. An experiment to evaluate this model was conducted using three case studies with same activities. The result show that the proposed model obtained those 5 out of 12 activities shows the power decreasing compared to others.