1. Reinforcement learning is also known as learning with critic?

a) yes

b) no

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2. How many types of reinforcement learning exist?

a) 2

b) 3

c) 4

d) 5

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3. What is fixed credit assignment?

a) reinforcement signal given to input-output pair don’t change with time

b) input-output pair determine probability of postive reinforcement

c) input pattern depends on past history

d) none of the mentioned

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4. What is probablistic credit assignment?

a) reinforcement signal given to input-output pair don’t change with time

b) input-output pair determine probability of postive reinforcement

c) input pattern depends on past history

d) none of the mentioned

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5. What is temporal credit assignment?

a) reinforcement signal given to input-output pair don’t change with time

b) input-output pair determine probability of postive reinforcement

c) input pattern depends on past history

d) none of the mentioned

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6. Boltzman learning uses what kind of learning?

a) deterministic

b) stochastic

c) either deterministic or stochastic

d) none of the mentioned

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7. Whats true for sparse encoding learning?

a) logical And & Or operations are used for input output relations

b) weight corresponds to minimum & maximum of units are connected

c) weights are expressed as linear combination of orthogonal basis vectors

d) change in weight uses a weighted sum of changes in past input values

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8. Whats true for Drive reinforcement learning?

a) logical And & Or operations are used for input output relations

b) weight corresponds to minimum & maximum of units are connected

c) weights are expressed as linear combination of orthogonal basis vectors

d) change in weight uses a weighted sum of changes in past input values

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9. Whats true for Min-max learning?

a) logical And & Or operations are used for input output relations

b) weight corresponds to minimum & maximum of units are connected

c) weights are expressed as linear combination of orthogonal basis vectors

d) change in weight uses a weighted sum of changes in past input values

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10. Whats true for principal component learning?

a) logical And & Or operations are used for input output relations

b) weight corresponds to minimum & maximum of units are connected

c) weights are expressed as linear combination of orthogonal basis vectors

d) change in weight uses a weighted sum of changes in past input values

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