Compare model-based vs model-free agents.
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Model-Based Agents
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Definition: These agents maintain an internal model of the environment, which includes knowledge about how the environment works and how actions affect states.
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How They Work:
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They observe the environment.
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Update their internal model.
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Use reasoning or planning to decide the next action.
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Strengths:
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Can handle partially observable environments because they remember past states.
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Good at planning and predicting future consequences of actions.
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More flexible and intelligent in complex environments.
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Weaknesses:
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Require more memory and computation to maintain and update the model.
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Slower response time compared to reactive approaches.
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Example: A self-driving car using maps, traffic rules, and sensor data to plan the safest route.
Definition: These agents maintain an internal model of the environment, which includes knowledge about how the environment works and how actions affect states.
How They Work:
-
They observe the environment.
-
Update their internal model.
-
Use reasoning or planning to decide the next action.
Strengths:
-
Can handle partially observable environments because they remember past states.
-
Good at planning and predicting future consequences of actions.
-
More flexible and intelligent in complex environments.
Weaknesses:
-
Require more memory and computation to maintain and update the model.
-
Slower response time compared to reactive approaches.
Example: A self-driving car using maps, traffic rules, and sensor data to plan the safest route.
Model-Free Agents
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Definition: These agents do not maintain a model of the environment. They decide actions based only on direct experiences or learned policies.
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How They Work:
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Act directly based on current perception (sometimes guided by learned values or policies).
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Learn behavior through trial and error without explicitly modeling the environment.
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Strengths:
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Simpler and faster, since they don’t spend resources on building or updating a model.
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Useful in real-time systems where immediate action is needed.
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Weaknesses:
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Limited ability to plan ahead.
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Struggle in partially observable or highly complex environments.
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Example: A robot vacuum that cleans by bumping into walls and adjusting direction without storing a map of the room.
Definition: These agents do not maintain a model of the environment. They decide actions based only on direct experiences or learned policies.
How They Work:
-
Act directly based on current perception (sometimes guided by learned values or policies).
-
Learn behavior through trial and error without explicitly modeling the environment.
Strengths:
-
Simpler and faster, since they don’t spend resources on building or updating a model.
-
Useful in real-time systems where immediate action is needed.
Weaknesses:
-
Limited ability to plan ahead.
-
Struggle in partially observable or highly complex environments.
Example: A robot vacuum that cleans by bumping into walls and adjusting direction without storing a map of the room.
Comparison Table
Aspect Model-Based Agents Model-Free Agents Internal Model Maintains one No model Decision Making Uses reasoning & planning Based on direct experience Computation High (more memory & processing) Low (simpler) Environment Handling Works well in partially observable environments Best for simple, fully observable environments Speed Slower (due to planning) Faster (immediate response) Example Self-driving car with maps Basic robot vacuum
| Aspect | Model-Based Agents | Model-Free Agents |
|---|---|---|
| Internal Model | Maintains one | No model |
| Decision Making | Uses reasoning & planning | Based on direct experience |
| Computation | High (more memory & processing) | Low (simpler) |
| Environment Handling | Works well in partially observable environments | Best for simple, fully observable environments |
| Speed | Slower (due to planning) | Faster (immediate response) |
| Example | Self-driving car with maps | Basic robot vacuum |
✅ In summary:
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Model-Based = Thinks before acting (planner, uses memory).
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Model-Free = Acts immediately (reactive, no memory of environment).
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