Looking for a proper reinforcement learning solution Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election Resultslearning rate in reinforcement learningCooperative Reinforcement LearningWhat is Reinforcement Learning?Understanding advantage functionsReinforcement learning, pendulum pythonReinforcement learning for continuous (rather than discrete) actionsReinforcement Learning different patientsCan Reinforcement Learning work for Dutch auctions?Reinforcement Learning - What's the formula for the value functionReinforcement Learning (Q Learning)
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Looking for a proper reinforcement learning solution
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election Resultslearning rate in reinforcement learningCooperative Reinforcement LearningWhat is Reinforcement Learning?Understanding advantage functionsReinforcement learning, pendulum pythonReinforcement learning for continuous (rather than discrete) actionsReinforcement Learning different patientsCan Reinforcement Learning work for Dutch auctions?Reinforcement Learning - What's the formula for the value functionReinforcement Learning (Q Learning)
$begingroup$
I am looking for a proper reinforcement learning solution for the following problem:
Suppose I have a pool of candidate functions f in Pool(it's like f1, f2, ... fn), and I am trying to synthesize several sequences of functions, each sequence contains arbitrary amount of f.
Given a sequence s, when deciding whether to extend s by appending a new function f and forming a new sequence s' = sf, I have the following reward scheme:
when F(s') = true, reward += 1
when T(s') = true, reward += 3
every time the sequence grows by one, I try to penalize by reward -= 0.5
In other words, I don't want each sequence s to be too long, but also will consider cases when special property F and T are satisfied.
I view this as a learning problem, and envision it can be solved by reinforcement learning techniques, such as DQN or so. But I am a newbie to this field and find it difficult for me to formalize this problem. Could anyone can shed some lights on this and what kind of reinforcement learning algorithms/scheme I can have a try? Thanks a lot. Any suggestion or advice will be appreciated.
reinforcement-learning
asked 28 mins ago
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add a comment |
$begingroup$
I am looking for a proper reinforcement learning solution for the following problem:
Suppose I have a pool of candidate functions f in Pool(it's like f1, f2, ... fn), and I am trying to synthesize several sequences of functions, each sequence contains arbitrary amount of f.
Given a sequence s, when deciding whether to extend s by appending a new function f and forming a new sequence s' = sf, I have the following reward scheme:
when F(s') = true, reward += 1
when T(s') = true, reward += 3
every time the sequence grows by one, I try to penalize by reward -= 0.5
In other words, I don't want each sequence s to be too long, but also will consider cases when special property F and T are satisfied.
I view this as a learning problem, and envision it can be solved by reinforcement learning techniques, such as DQN or so. But I am a newbie to this field and find it difficult for me to formalize this problem. Could anyone can shed some lights on this and what kind of reinforcement learning algorithms/scheme I can have a try? Thanks a lot. Any suggestion or advice will be appreciated.
reinforcement-learning
asked 28 mins ago
llllllllllllllllllllllllll
1012
New contributor
lllllllllllll 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 am looking for a proper reinforcement learning solution for the following problem:
Suppose I have a pool of candidate functions f in Pool(it's like f1, f2, ... fn), and I am trying to synthesize several sequences of functions, each sequence contains arbitrary amount of f.
Given a sequence s, when deciding whether to extend s by appending a new function f and forming a new sequence s' = sf, I have the following reward scheme:
when F(s') = true, reward += 1
when T(s') = true, reward += 3
every time the sequence grows by one, I try to penalize by reward -= 0.5
In other words, I don't want each sequence s to be too long, but also will consider cases when special property F and T are satisfied.
I view this as a learning problem, and envision it can be solved by reinforcement learning techniques, such as DQN or so. But I am a newbie to this field and find it difficult for me to formalize this problem. Could anyone can shed some lights on this and what kind of reinforcement learning algorithms/scheme I can have a try? Thanks a lot. Any suggestion or advice will be appreciated.
reinforcement-learning
asked 28 mins ago
llllllllllllllllllllllllll
1012
New contributor
lllllllllllll is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I am looking for a proper reinforcement learning solution for the following problem:
Suppose I have a pool of candidate functions f in Pool(it's like f1, f2, ... fn), and I am trying to synthesize several sequences of functions, each sequence contains arbitrary amount of f.
Given a sequence s, when deciding whether to extend s by appending a new function f and forming a new sequence s' = sf, I have the following reward scheme:
when F(s') = true, reward += 1
when T(s') = true, reward += 3
every time the sequence grows by one, I try to penalize by reward -= 0.5
In other words, I don't want each sequence s to be too long, but also will consider cases when special property F and T are satisfied.
I view this as a learning problem, and envision it can be solved by reinforcement learning techniques, such as DQN or so. But I am a newbie to this field and find it difficult for me to formalize this problem. Could anyone can shed some lights on this and what kind of reinforcement learning algorithms/scheme I can have a try? Thanks a lot. Any suggestion or advice will be appreciated.
reinforcement-learning
reinforcement-learning
asked 28 mins ago
llllllllllllllllllllllllll
1012
New contributor
lllllllllllll is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 28 mins ago
llllllllllllllllllllllllll
1012
New contributor
lllllllllllll is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 28 mins ago
llllllllllllllllllllllllll
1012
New contributor
lllllllllllll is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 28 mins ago
llllllllllllllllllllllllll
1012
asked 28 mins ago
llllllllllllllllllllllllll
1012
1012
New contributor
lllllllllllll is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
lllllllllllll is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
lllllllllllll is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
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