KoGuN: Accelerating Deep Reinforcement Learning via Integrating Human Suboptimal Knowledge

Understand

Reinforcement learning agents usually learn from scratch, which requires a large number of interactions with the environment.

• This is quite different from the learning process of human.
• When faced with a new task, human naturally have the common sense and use the prior knowledge to derive an initial policy and guide the learning process afterwards.
• Although the prior knowledge may be not fully applicable to the new task, the learning process is significantly sped up since the initial policy ensures a quick-start of learning and intermediate guidance allows to avoid unnecessary exploration.