Numerical Computing with Python

Fundamental categories in sequential decision making

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 ...