Control systems and reinforcement learning

Can reinforcement learning be used for control problems?
Reinforcement learning (RL) is a model-free framework for solving optimal control problems stated as Markov decision processes (MDPs) (Puterman, 1994)..
How reinforcement learning is used in control systems?
You can also use reinforcement learning to create an end-to-end controller that generates actions directly from raw data, such as images.
This approach is attractive for video-intensive applications, such as automated driving, since you do not have to manually define and select image features..
Is reinforcement learning just control theory?
Reinforcement Learning is a field closely related to control theory.
Its formalism is a little different, and its techniques are traditionally associated with machine learning.
These days it's dominated by the use of deep neural networks..
What is control in reinforcement learning?
A control task in RL is where the policy is not fixed, and the goal is to find the optimal policy.
That is, to find the policy π(as) that maximises the expected total reward from any given state..
What is learning control system?
Learning control implies that the control system contains sufficient computational ability so that it can develop representations of the mathematical model of the system being controlled and can modify its own operation to take advantage of this newly developed knowledge..
A "control policy" is a heuristic that suggests a particular set of actions in response to the current state of the agent (in your case, a robot) and the environment.
In the case of reinforcement learning, a policy is parameterized by the network weights.
A control condition that separates the effects of stimulus presentation from those produced by a positive reinforcement contingency would involve continued presentation of stimuli delivered in the experimental condition in the absence of a contingency between the target response and the presentation of those stimuli.

Reinforcement learning is a collection of tools for the design of decision and control algorithms. What makes RL different from traditional control is that the modelling step is avoided, and instead the control design is based on observations of the system to be controlled.

How do you approximate a Q-function in reinforcement learning?

Within the control systems literature, there are dynamic programming techniques to approximate the Q-function that appears in reinforcement learning

In particular, this “value function” is the solution to a simple convex program (an example is the “DPLP” appearing in Eq

(3 36))

How does reinforcement learning work in a control system?

The behavior of a reinforcement learning policy—that is, how the policy observes the environment and generates actions to complete a task in an optimal manner—is similar to the operation of a controller in a control system

Reinforcement learning can be translated to a control system representation using the following mapping

Who should read the Handbook of reinforcement learning and control?

Practicing engineers and scholars in the field of machine learning, game theory, and autonomous control will find the Handbook of Reinforcement Learning and Control to be thought-provoking, instructive and informative

Kyriakos G

Vamvoudakis serves as an Assistant Professor at The Daniel Guggenheim School of Aerospace Engineering at Georgia Tech

Control systems and reinforcement learning

Can reinforcement learning be used for control problems?

How reinforcement learning is used in control systems?

Is reinforcement learning just control theory?

What is control in reinforcement learning?

What is learning control system?

How do you approximate a Q-function in reinforcement learning?

How does reinforcement learning work in a control system?

Who should read the Handbook of reinforcement learning and control?