Action space refers to the set of all possible actions that an agent can take in a given environment, crucial for decision-making processes in fields like reinforcement learning and control theory. Understanding the structure and constraints of an action space is essential for designing algorithms that can effectively navigate and optimize within complex systems.

Action Space

Action space

possible actions

An agent is an entity capable of perceiving its environment through sensors and acting upon it through actuators to achieve specific goals. Agents can be found in various domains, including artificial intelligence, economics, and philosophy, where they are characterized by autonomy, social ability, reactivity, and proactiveness.

agent

Decision-making processes involve selecting the best course of action from multiple alternatives based on available information, preferences, and values. Effective decision-making requires balancing rational analysis with intuitive judgment to achieve desired outcomes and mitigate potential risks.

decision-making processes

Reinforcement learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize cumulative reward. It involves trial and error, exploration, and exploitation to develop an optimal strategy or policy for decision-making tasks.

reinforcement learning

Control theory is a field of study that focuses on the behavior of dynamical systems and the use of feedback to modify the behavior of these systems to achieve desired outcomes. It is widely applied in engineering and science to design systems that maintain stability and performance despite external disturbances and uncertainties.

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