What is the difference between reinforcement learning and optimization?

What is the difference between reinforcement learning and optimization?

In essence, Reinforcement Learning is a data driven approach, where the optimization process is achieved by agent-environment interaction (i.e., data). On the other hand, Optimisation Research uses other methods that require deeper knowledge of the problem and/or imposes more assumptions.

What is reinforcement learning control?

Reinforcement learning (RL) is a model-free framework for solving optimal control problems stated as Markov decision processes (MDPs) (Puterman, 1994). MDPs work in discrete time: at each time step, the controller receives feedback from the system in the form of a state signal, and takes an action in re- sponse.

What is optimal control system?

Optimal control is the process of determining control and state trajectories for a dynamic system over a period of time to minimise a performance index.

Is Reinforcement a learning optimization problem?

Defining the function itself in Reinforcement Learning (RL) is a bit tricky. People often get confused about what RL actually does. Well, it does nothing but optimization. It does not directly do any kind of classification, regression or clustering which are typical ML methods.

How do you find optimal control?

The optimal control can be derived using Pontryagin’s maximum principle (a necessary condition also known as Pontryagin’s minimum principle or simply Pontryagin’s Principle), or by solving the Hamilton–Jacobi–Bellman equation (a sufficient condition).

How is reinforcement learning related to adaptive control?

Reinforcement learning is connected from a theoretical point of view with both adaptive control and optimal control methods. One type of reinforcement learning algorithms employs the actor-critic structure shown in Figure 1 [16].

Which is the same book as reinforcement learning?

The same book Reinforcement learning: an introduction (2nd edition, 2018) by Sutton and Barto has a section, 1.7 Early History of Reinforcement Learning, that describes what optimal control is and how it is related to reinforcement learning.

What’s the difference between optimal control and RL?

As has been pointed out by comments and answers here (as well as the OP), the line between RL and optimal control can be quite blurry. Consider the Linear-Quadratic-Gaussian (LQG) algorithm, which is generally considered to be an optimal control method.

What does reinforcement learning mean to an agent?

Reinforcement learning implies a cause-and-effect rela- tionship between actions and reward or punishment. It implies goal-directed behavior, at least insofar as the agent has an understanding of reward versus lack of reward or punishment.