  For most reinforcement learning approaches, the learning is performed by
maximizing an accumulative reward that is expectedly and manually defined for
specific tasks. However, in real world, rewards are emergent phenomena from the
complex interactions between agents and environments. In this paper, we propose
an implicit generic reward model for reinforcement learning. Unlike those
rewards that are manually defined for specific tasks, such implicit reward is
task independent. It only comes from the deviation from the agents' previous
experiences.
