  The initial years of an infant's life are known as the critical period,
during which the overall development of learning performance is significantly
impacted due to neural plasticity. In recent studies, an AI agent, with a deep
neural network mimicking mechanisms of actual neurons, exhibited a learning
period similar to human's critical period. Especially during this initial
period, the appropriate stimuli play a vital role in developing learning
ability. However, transforming human cognitive bias into an appropriate shaping
reward is quite challenging, and prior works on critical period do not focus on
finding the appropriate stimulus. To take a step further, we propose
multi-stage reinforcement learning to emphasize finding ``appropriate stimulus"
around the critical period. Inspired by humans' early cognitive-developmental
stage, we use multi-stage guidance near the critical period, and demonstrate
the appropriate shaping reward (stage-2 guidance) in terms of the AI agent's
performance, efficiency, and stability.
