Evaluating value functions in RL
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I'm working my way through the book Reinforcement Learning by Richar S. Sutton and Andrew G. Barto and I am stuck on the following question.
The value of a state depends on the the values of the actions possible in that state and on how likely each action is to be taken under the current policy.
We can think of this in terms of a small backup diagram rooted at the state and considering each possible action:
Give the equation corresponding to this intuition and diagram for the value at the root node
, in terms of the value at the expected leaf node,
, given
. This expectation depends on the policy,
. Then give a second equation in which the expected value is written out explicitly in terms of
such that no expected value notation appears in the equation.
I should mention that...
...
Where...
= Probability of taking action a from state s
= Given any state s and a, the probability of each next state s'
= Expected reward given any state s, next state s, and action a
How can I re-evaluate this value function in the way that is asked?
reinforcement-learning markov-process monte-carlo
asked 54 secs ago
BolboaBolboa
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$begingroup$
I'm working my way through the book Reinforcement Learning by Richar S. Sutton and Andrew G. Barto and I am stuck on the following question.
The value of a state depends on the the values of the actions possible in that state and on how likely each action is to be taken under the current policy.
We can think of this in terms of a small backup diagram rooted at the state and considering each possible action:
Give the equation corresponding to this intuition and diagram for the value at the root node
, in terms of the value at the expected leaf node,
, given
. This expectation depends on the policy,
. Then give a second equation in which the expected value is written out explicitly in terms of
such that no expected value notation appears in the equation.
I should mention that...
...
Where...
= Probability of taking action a from state s
= Given any state s and a, the probability of each next state s'
= Expected reward given any state s, next state s, and action a
How can I re-evaluate this value function in the way that is asked?
reinforcement-learning markov-process monte-carlo
asked 54 secs ago
BolboaBolboa
1011
New contributor
Bolboa is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
I'm working my way through the book Reinforcement Learning by Richar S. Sutton and Andrew G. Barto and I am stuck on the following question.
The value of a state depends on the the values of the actions possible in that state and on how likely each action is to be taken under the current policy.
We can think of this in terms of a small backup diagram rooted at the state and considering each possible action:
Give the equation corresponding to this intuition and diagram for the value at the root node
, in terms of the value at the expected leaf node,
, given
. This expectation depends on the policy,
. Then give a second equation in which the expected value is written out explicitly in terms of
such that no expected value notation appears in the equation.
I should mention that...
...
Where...
= Probability of taking action a from state s
= Given any state s and a, the probability of each next state s'
= Expected reward given any state s, next state s, and action a
How can I re-evaluate this value function in the way that is asked?
reinforcement-learning markov-process monte-carlo
asked 54 secs ago
BolboaBolboa
1011
New contributor
Bolboa is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I'm working my way through the book Reinforcement Learning by Richar S. Sutton and Andrew G. Barto and I am stuck on the following question.
The value of a state depends on the the values of the actions possible in that state and on how likely each action is to be taken under the current policy.
We can think of this in terms of a small backup diagram rooted at the state and considering each possible action:
Give the equation corresponding to this intuition and diagram for the value at the root node
, in terms of the value at the expected leaf node,
, given
. This expectation depends on the policy,
. Then give a second equation in which the expected value is written out explicitly in terms of
such that no expected value notation appears in the equation.
I should mention that...
...
Where...
= Probability of taking action a from state s
= Given any state s and a, the probability of each next state s'
= Expected reward given any state s, next state s, and action a
How can I re-evaluate this value function in the way that is asked?
reinforcement-learning markov-process monte-carlo
reinforcement-learning markov-process monte-carlo
asked 54 secs ago
BolboaBolboa
1011
New contributor
Bolboa is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 54 secs ago
BolboaBolboa
1011
New contributor
Bolboa is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 54 secs ago
BolboaBolboa
1011
New contributor
Bolboa is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 54 secs ago
BolboaBolboa
1011
asked 54 secs ago
BolboaBolboa
1011
1011
New contributor
Bolboa is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Bolboa is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Bolboa is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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