Action in ML - Python Automation and Machine Learning for ICs - - An Online Book - |
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Python Automation and Machine Learning for ICs http://www.globalsino.com/ICs/ | ||||||||
Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix | ||||||||
================================================================================= An action refers to the choices or decisions that an agent can make in a given state. In other words, actions are the ways in which the agent can influence or interact with the environment. The set of possible actions depends on the specific problem or task. The goal of the agent is often to learn a policy, a mapping from states to actions, that maximizes some notion of cumulative reward over time. For instance, in a game-playing scenario, the state could be the current board position, and the actions could be the possible moves that the player can make in that state. In a robotic control scenario, the state might represent the current sensor readings, and the actions could be different motor commands the robot can execute.
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