What are features for state-action pairs in RL?What are features in the context of reinforcement learning?What is the Q function and what is the V function in reinforcement learning?Reward dependent on (state, action) versus (state, action, successor state)Semi-gradient TD(0) Choosing an ActionReinforcement Learning algorithm for Optimized Trade Executionstate-action-reward-new state: confusion of termsDefining State Representation in Deep Q-LearningWhat are features in the context of reinforcement learning?Reinforcement learning - How to deal with varying number of actions which do number approximationReinforcement learning for continuous state and action spaceHow to write out the definition of the value function for continous action and state space
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What are features for state-action pairs in RL?
What are features in the context of reinforcement learning?What is the Q function and what is the V function in reinforcement learning?Reward dependent on (state, action) versus (state, action, successor state)Semi-gradient TD(0) Choosing an ActionReinforcement Learning algorithm for Optimized Trade Executionstate-action-reward-new state: confusion of termsDefining State Representation in Deep Q-LearningWhat are features in the context of reinforcement learning?Reinforcement learning - How to deal with varying number of actions which do number approximationReinforcement learning for continuous state and action spaceHow to write out the definition of the value function for continous action and state space
$begingroup$
I read this answer: What are features in the context of reinforcement learning?
But it only describes features for the state only in the context of cartpole, ie. Cart Position, Cart Velocity, Pole Angle, Pole Velocity At Tip
On slide 18 here: http://www.cs.cmu.edu/~rsalakhu/10703/Lecture_VFA.pdf
It states:
But does not give examples. I started reading from p. 198 in Sutton's book for Value Function Approximation but also did not see examples for "features of state-action pairs" .
My best guess is for example in Cartpole-V1 (discrete action space) would be to add one more number to the tuple describing the state-action pair, ie. (Cart Position, Cart Velocity, Pole Angle, Pole Velocity At Tip, push_right) .
In the case of Cartpole I guess each state action pair could be described with a feature vector of length 3 where the final input for the tuple is either "push_left", "do_nothing", "push_right".
Would the immediate reward from taking one of the actions also be included in the tuples that form the state-action feature vector?
reinforcement-learning feature-construction
edited 2 days ago
Philip Raeisghasem
1735
asked 2 days ago
flexitarian33flexitarian33
256
New contributor
flexitarian33 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I read this answer: What are features in the context of reinforcement learning?
But it only describes features for the state only in the context of cartpole, ie. Cart Position, Cart Velocity, Pole Angle, Pole Velocity At Tip
On slide 18 here: http://www.cs.cmu.edu/~rsalakhu/10703/Lecture_VFA.pdf
It states:
But does not give examples. I started reading from p. 198 in Sutton's book for Value Function Approximation but also did not see examples for "features of state-action pairs" .
My best guess is for example in Cartpole-V1 (discrete action space) would be to add one more number to the tuple describing the state-action pair, ie. (Cart Position, Cart Velocity, Pole Angle, Pole Velocity At Tip, push_right) .
In the case of Cartpole I guess each state action pair could be described with a feature vector of length 3 where the final input for the tuple is either "push_left", "do_nothing", "push_right".
Would the immediate reward from taking one of the actions also be included in the tuples that form the state-action feature vector?
reinforcement-learning feature-construction
edited 2 days ago
Philip Raeisghasem
1735
asked 2 days ago
flexitarian33flexitarian33
256
New contributor
flexitarian33 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
1
$begingroup$
Your questions about David Silver's policy gradient lecture should be posted separately. He wasn't talking about feature construction. He was talking about how the policy is parameterized and learned.
$endgroup$
– Philip Raeisghasem
2 days ago
$begingroup$
Hey didn't realize I was off topic as I was just trying to show my chain-of-thought and what I was concurrently looking at, ie. I was trying to find some common ground for gradients across a wide range of algorithms.
$endgroup$
– flexitarian33
2 days ago
$begingroup$
No problem! If you have questions about policy gradients you can't find the answers to, I or someone else here would be happy to answer them.
$endgroup$
– Philip Raeisghasem
2 days ago
add a comment |
$begingroup$
I read this answer: What are features in the context of reinforcement learning?
But it only describes features for the state only in the context of cartpole, ie. Cart Position, Cart Velocity, Pole Angle, Pole Velocity At Tip
On slide 18 here: http://www.cs.cmu.edu/~rsalakhu/10703/Lecture_VFA.pdf
It states:
But does not give examples. I started reading from p. 198 in Sutton's book for Value Function Approximation but also did not see examples for "features of state-action pairs" .
My best guess is for example in Cartpole-V1 (discrete action space) would be to add one more number to the tuple describing the state-action pair, ie. (Cart Position, Cart Velocity, Pole Angle, Pole Velocity At Tip, push_right) .
In the case of Cartpole I guess each state action pair could be described with a feature vector of length 3 where the final input for the tuple is either "push_left", "do_nothing", "push_right".
Would the immediate reward from taking one of the actions also be included in the tuples that form the state-action feature vector?
reinforcement-learning feature-construction
edited 2 days ago
Philip Raeisghasem
1735
asked 2 days ago
flexitarian33flexitarian33
256
New contributor
flexitarian33 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I read this answer: What are features in the context of reinforcement learning?
But it only describes features for the state only in the context of cartpole, ie. Cart Position, Cart Velocity, Pole Angle, Pole Velocity At Tip
On slide 18 here: http://www.cs.cmu.edu/~rsalakhu/10703/Lecture_VFA.pdf
It states:
But does not give examples. I started reading from p. 198 in Sutton's book for Value Function Approximation but also did not see examples for "features of state-action pairs" .
My best guess is for example in Cartpole-V1 (discrete action space) would be to add one more number to the tuple describing the state-action pair, ie. (Cart Position, Cart Velocity, Pole Angle, Pole Velocity At Tip, push_right) .
In the case of Cartpole I guess each state action pair could be described with a feature vector of length 3 where the final input for the tuple is either "push_left", "do_nothing", "push_right".
Would the immediate reward from taking one of the actions also be included in the tuples that form the state-action feature vector?
reinforcement-learning feature-construction
reinforcement-learning feature-construction
edited 2 days ago
Philip Raeisghasem
1735
asked 2 days ago
flexitarian33flexitarian33
256
New contributor
flexitarian33 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited 2 days ago
Philip Raeisghasem
1735
asked 2 days ago
flexitarian33flexitarian33
256
New contributor
flexitarian33 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited 2 days ago
Philip Raeisghasem
1735
edited 2 days ago
Philip Raeisghasem
1735
edited 2 days ago
Philip Raeisghasem
1735
1735
asked 2 days ago
flexitarian33flexitarian33
256
New contributor
flexitarian33 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
flexitarian33flexitarian33
256
asked 2 days ago
flexitarian33flexitarian33
256
256
New contributor
flexitarian33 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
flexitarian33 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
flexitarian33 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
1
$begingroup$
Your questions about David Silver's policy gradient lecture should be posted separately. He wasn't talking about feature construction. He was talking about how the policy is parameterized and learned.
$endgroup$
– Philip Raeisghasem
2 days ago
$begingroup$
Hey didn't realize I was off topic as I was just trying to show my chain-of-thought and what I was concurrently looking at, ie. I was trying to find some common ground for gradients across a wide range of algorithms.
$endgroup$
– flexitarian33
2 days ago
$begingroup$
No problem! If you have questions about policy gradients you can't find the answers to, I or someone else here would be happy to answer them.
$endgroup$
– Philip Raeisghasem
2 days ago
add a comment |
1
$begingroup$
Your questions about David Silver's policy gradient lecture should be posted separately. He wasn't talking about feature construction. He was talking about how the policy is parameterized and learned.
$endgroup$
– Philip Raeisghasem
2 days ago
$begingroup$
Hey didn't realize I was off topic as I was just trying to show my chain-of-thought and what I was concurrently looking at, ie. I was trying to find some common ground for gradients across a wide range of algorithms.
$endgroup$
– flexitarian33
2 days ago
$begingroup$
No problem! If you have questions about policy gradients you can't find the answers to, I or someone else here would be happy to answer them.
$endgroup$
– Philip Raeisghasem
2 days ago
1
1
$begingroup$
Your questions about David Silver's policy gradient lecture should be posted separately. He wasn't talking about feature construction. He was talking about how the policy is parameterized and learned.
$endgroup$
– Philip Raeisghasem
2 days ago
$begingroup$
Your questions about David Silver's policy gradient lecture should be posted separately. He wasn't talking about feature construction. He was talking about how the policy is parameterized and learned.
$endgroup$
– Philip Raeisghasem
2 days ago
$begingroup$
Hey didn't realize I was off topic as I was just trying to show my chain-of-thought and what I was concurrently looking at, ie. I was trying to find some common ground for gradients across a wide range of algorithms.
$endgroup$
– flexitarian33
2 days ago
$begingroup$
Hey didn't realize I was off topic as I was just trying to show my chain-of-thought and what I was concurrently looking at, ie. I was trying to find some common ground for gradients across a wide range of algorithms.
$endgroup$
– flexitarian33
2 days ago
$begingroup$
No problem! If you have questions about policy gradients you can't find the answers to, I or someone else here would be happy to answer them.
$endgroup$
– Philip Raeisghasem
2 days ago
$begingroup$
No problem! If you have questions about policy gradients you can't find the answers to, I or someone else here would be happy to answer them.
$endgroup$
– Philip Raeisghasem
2 days ago
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
In the cartpole example, a state-action feature would be
$$beginbmatrix
textCart Position\
textCart Velocity\
textPole Angle\
textPole Tip Velocity\
textAction
endbmatrix$$
where Action is either left, right, or do nothing. The reward is not part of the feature vector because reward does not describe the state of the agent; it is not an input. It is a (possibly stochastic) signal received from the environment that the agent is trying to predict/control with the use of feature vectors.
answered 2 days ago
Philip RaeisghasemPhilip Raeisghasem
1735
New contributor
Philip Raeisghasem is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
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$begingroup$
In the cartpole example, a state-action feature would be
$$beginbmatrix
textCart Position\
textCart Velocity\
textPole Angle\
textPole Tip Velocity\
textAction
endbmatrix$$
where Action is either left, right, or do nothing. The reward is not part of the feature vector because reward does not describe the state of the agent; it is not an input. It is a (possibly stochastic) signal received from the environment that the agent is trying to predict/control with the use of feature vectors.
answered 2 days ago
Philip RaeisghasemPhilip Raeisghasem
1735
New contributor
Philip Raeisghasem is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
In the cartpole example, a state-action feature would be
$$beginbmatrix
textCart Position\
textCart Velocity\
textPole Angle\
textPole Tip Velocity\
textAction
endbmatrix$$
where Action is either left, right, or do nothing. The reward is not part of the feature vector because reward does not describe the state of the agent; it is not an input. It is a (possibly stochastic) signal received from the environment that the agent is trying to predict/control with the use of feature vectors.
answered 2 days ago
Philip RaeisghasemPhilip Raeisghasem
1735
New contributor
Philip Raeisghasem is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
In the cartpole example, a state-action feature would be
$$beginbmatrix
textCart Position\
textCart Velocity\
textPole Angle\
textPole Tip Velocity\
textAction
endbmatrix$$
where Action is either left, right, or do nothing. The reward is not part of the feature vector because reward does not describe the state of the agent; it is not an input. It is a (possibly stochastic) signal received from the environment that the agent is trying to predict/control with the use of feature vectors.
answered 2 days ago
Philip RaeisghasemPhilip Raeisghasem
1735
New contributor
Philip Raeisghasem is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
In the cartpole example, a state-action feature would be
$$beginbmatrix
textCart Position\
textCart Velocity\
textPole Angle\
textPole Tip Velocity\
textAction
endbmatrix$$
where Action is either left, right, or do nothing. The reward is not part of the feature vector because reward does not describe the state of the agent; it is not an input. It is a (possibly stochastic) signal received from the environment that the agent is trying to predict/control with the use of feature vectors.
answered 2 days ago
Philip RaeisghasemPhilip Raeisghasem
1735
New contributor
Philip Raeisghasem is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 2 days ago
Philip RaeisghasemPhilip Raeisghasem
1735
New contributor
Philip Raeisghasem is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 2 days ago
Philip RaeisghasemPhilip Raeisghasem
1735
answered 2 days ago
Philip RaeisghasemPhilip Raeisghasem
1735
1735
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New contributor
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1
$begingroup$
Your questions about David Silver's policy gradient lecture should be posted separately. He wasn't talking about feature construction. He was talking about how the policy is parameterized and learned.
$endgroup$
– Philip Raeisghasem
2 days ago
$begingroup$
Hey didn't realize I was off topic as I was just trying to show my chain-of-thought and what I was concurrently looking at, ie. I was trying to find some common ground for gradients across a wide range of algorithms.
$endgroup$
– flexitarian33
2 days ago
$begingroup$
No problem! If you have questions about policy gradients you can't find the answers to, I or someone else here would be happy to answer them.
$endgroup$
– Philip Raeisghasem
2 days ago