There are two fundamental types of problems in sequential decision making:

• Reinforcement learning (for example, autonomous helicopter, and so on):
  • Environment is initially unknown
  • Agent interacts with the environment and obtain policies, rewards, values from the environment
  • Agent improves its policy
• Planning (for example, chess, Atari games, and so on):
  • Model of environment or complete dynamics of environment is known
  • Agent performs computation with its model (without any external interaction)
  • Agent improves its policy
  • These are the type of problems also known as reasoning, searching, introspection, and so on

Though the preceding two categories can be linked together as per the given problem, but this is basically a broad view of the two types of setups.