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Passive Reinforcement Learning

Theory

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Passive Reinforcement Learning

Passive Reinforcement Learning is about choosing a sequence of actions toward a goal, sometimes with other agents or chance. State the goal, the actions, and whether the world is deterministic. Planning ≠ classification.

Output — idea / do / check for Passive Reinforcement Learning. Fill those three in the viva.

Tiny Passive Reinforcement Learning story: get the key, open the door, reach the gold. That beats a UML diagram.

Exam tip

Goal + actions + deterministic or not.

Example

# Passive Reinforcement Learning
print("idea :", "Passive Reinforcement Learning")
print("do   : one tiny example on paper")
print("check: one failure case")

Output: idea / do / check for Passive Reinforcement Learning. Fill those three in the viva.

Short notes

DefPassive Reinforcement Learning — actions toward a goal.
RuleState the world type.
TrapPassive Reinforcement Learning — calling every ML model “planning”.

AI · Theory

Passive Reinforcement Learning

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Theory

92/215

Passive Reinforcement Learning

Output — idea / do / check for Passive Reinforcement Learning. Fill those three in the viva.

Tiny Passive Reinforcement Learning story: get the key, open the door, reach the gold. That beats a UML diagram.

Exam tip

Goal + actions + deterministic or not.

Example

# Passive Reinforcement Learning
print("idea :", "Passive Reinforcement Learning")
print("do   : one tiny example on paper")
print("check: one failure case")

Output: idea / do / check for Passive Reinforcement Learning. Fill those three in the viva.

Short notes

DefPassive Reinforcement Learning — actions toward a goal.
RuleState the world type.
TrapPassive Reinforcement Learning — calling every ML model “planning”.

Passive Reinforcement Learning

Example

Short notes

Questions

Passive Reinforcement Learning

Example

Short notes

Questions