What is reinforcement learning

a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex and uncertain environment.

Difference between Reinforcement Learning and Supervised Learning

• Sequential data as input(not i.i.d) 序列化输入
• The learner is not told which actions to take, but instead must discover which actions yield the most reward by trying them.
• Trial-and-error exploration(balance between exploration and exploitation)
• There is no supervisor, only a reward signal, which is also delayed.

Features of Reinforcement Learning

• Trial-and-error exploration
• Delayed reward
• Time matters (sequential data, non i.i.d data)
• Agent’s actions affect the subsequent data it receives (agent’s action changes the environment)