IB Computer Science Topic A4.3.6 Agents Learning to Make Decisions Revision Notes (HL)

IB Computer Science Topic A4.3.6 Agents Learning to Make Decisions Revision Notes (HL) | RevisionDojo

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How an Agent Learns to Make Decisions by Interacting with Its Environment in Reinforcement Learning

The Agent-Environment Interaction

Agent: The entity that makes decisions.
Environment: The context in which the agent operates.
States: The current situation or configuration of the environment.
Actions: The choices available to the agent.
Rewards: Feedback from the environment evaluating the agent's actions.
Policies: Strategies that map states to actions.

Note

The state space is the set of all possible states, while the action space is the set of all possible actions.

Cumulative Reward

Definition: The total reward an agent accumulates over time.
Goal: Maximize this reward by making optimal decisions.

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Definition

Paywall

(on a website) an arrangement whereby access is restricted to users who have paid to subscribe to the site.

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Duis aute irure dolor in reprehenderit

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Note

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Excepteur sint occaecat cupidatat non proident

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Tip

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.

Lorem ipsum dolor sit amet, consectetur adipiscing elit.
Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris.
Duis aute irure dolor in reprehenderit in voluptate velit esse cillum.

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How an Agent Learns to Make Decisions by Interacting with Its Environment in Reinforcement Learning

The Agent-Environment Interaction

Agent: The entity that makes decisions.
Environment: The context in which the agent operates.
States: The current situation or configuration of the environment.
Actions: The choices available to the agent.
Rewards: Feedback from the environment evaluating the agent's actions.
Policies: Strategies that map states to actions.

Note

The state space is the set of all possible states, while the action space is the set of all possible actions.

Cumulative Reward

Definition: The total reward an agent accumulates over time.
Goal: Maximize this reward by making optimal decisions.

Definition

Paywall

(on a website) an arrangement whereby access is restricted to users who have paid to subscribe to the site.

anim nostrud sit dolore minim proident quis fugiat velit et eiusmod nulla quis nulla mollit dolor sunt culpa aliqua

Duis aute irure dolor in reprehenderit

Note

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam quis nostrud exercitation.

Excepteur sint occaecat cupidatat non proident

Tip

Lorem ipsum dolor sit amet, consectetur adipiscing elit.
Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris.
Duis aute irure dolor in reprehenderit in voluptate velit esse cillum.

A1 Computer fundamentals4 subtopics

A2 Networks4 subtopics

A3 Databases4 subtopics

A4 Machine learning4 subtopics

B1 Computational thinking1 subtopic

B2 Programming5 subtopics

B3 Object-oriented programming2 subtopics

B4 Abstract data types (HL only)1 subtopic

IB Computer Science Topic A4.3.6: Agents Learning to Make Decisions Notes

A1 Computer fundamentals4 subtopics

A2 Networks4 subtopics

A3 Databases4 subtopics

A4 Machine learning4 subtopics

B1 Computational thinking1 subtopic

B2 Programming5 subtopics

B3 Object-oriented programming2 subtopics

B4 Abstract data types (HL only)1 subtopic

How an Agent Learns to Make Decisions by Interacting with Its Environment in Reinforcement Learning

The Agent-Environment Interaction

Cumulative Reward

anim nostrud sit dolore minim proident quis fugiat velit et eiusmod nulla quis nulla mollit dolor sunt culpa aliqua

Duis aute irure dolor in reprehenderit

Excepteur sint occaecat cupidatat non proident

Unlock the rest of this chapter with a free account

How an Agent Learns to Make Decisions by Interacting with Its Environment in Reinforcement Learning

The Agent-Environment Interaction

Cumulative Reward

anim nostrud sit dolore minim proident quis fugiat velit et eiusmod nulla quis nulla mollit dolor sunt culpa aliqua

Duis aute irure dolor in reprehenderit

Excepteur sint occaecat cupidatat non proident

Unlock the rest of this chapter with a free account