Suggestion of some courses in sequential decision makingOpen source Markov decision process solversReference request: how to model nonlinear regression?What is the connection of Operations Research and Reinforcement Learning?Single reference for Mixed Integer Programming formulations to linearize, handle logical constraints and disjunctive constraints, do Big M, etc?Recommended books/materials for practical applications of Operations Research in industryReal-world examples of supply chain contracts?
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Suggestion of some courses in sequential decision making
Open source Markov decision process solversReference request: how to model nonlinear regression?What is the connection of Operations Research and Reinforcement Learning?Single reference for Mixed Integer Programming formulations to linearize, handle logical constraints and disjunctive constraints, do Big M, etc?Recommended books/materials for practical applications of Operations Research in industryReal-world examples of supply chain contracts?
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I am studying about sequential decision making and I am willing to know if there is any course which is recorded and is publically available covering topics in dynamic programming (DP), reinforcement learning (RL), bandit problem, approximate DPRL, online optimization?
Thanks
reference-request online-resources reinforcement-learning sequential-decision-making dynamic-programming
$endgroup$
add a comment |
$begingroup$
I am studying about sequential decision making and I am willing to know if there is any course which is recorded and is publically available covering topics in dynamic programming (DP), reinforcement learning (RL), bandit problem, approximate DPRL, online optimization?
Thanks
reference-request online-resources reinforcement-learning sequential-decision-making dynamic-programming
$endgroup$
add a comment |
$begingroup$
I am studying about sequential decision making and I am willing to know if there is any course which is recorded and is publically available covering topics in dynamic programming (DP), reinforcement learning (RL), bandit problem, approximate DPRL, online optimization?
Thanks
reference-request online-resources reinforcement-learning sequential-decision-making dynamic-programming
$endgroup$
I am studying about sequential decision making and I am willing to know if there is any course which is recorded and is publically available covering topics in dynamic programming (DP), reinforcement learning (RL), bandit problem, approximate DPRL, online optimization?
Thanks
reference-request online-resources reinforcement-learning sequential-decision-making dynamic-programming
reference-request online-resources reinforcement-learning sequential-decision-making dynamic-programming
edited Jul 1 at 11:36
Marcus Ritt
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2,0565 silver badges29 bronze badges
asked Jun 30 at 8:31
Amin ShAmin Sh
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2 Answers
2
active
oldest
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$begingroup$
There are a few courses on Coursera that offer such learning materials.
Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming (Intermediate)
The primary topics in this part of the specialization are: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, optimal search trees).
If you want to go directly to dynamic programming then you can skip to weeks 3 and 4 of the syllabus.
Practical Reinforcement Learning (Advanced)
Here you will find out about:
foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc. - with math & batteries included
using deep neural networks for RL tasks - also known as "the hype train"
state of the art RL algorithms - and how to apply duct tape to them for practical problems.
and, of course, teaching your neural network to play games - because that's what everyone thinks RL is about. We'll also use it for seq2seq and contextual bandits.
$endgroup$
add a comment |
$begingroup$
AFAIK, There are some examples on the YouTube host. For instance, this link.
$endgroup$
$begingroup$
This is a link to a search term of the main words in the title. While this could be a decent suggestion, I don't think this is a proper answer. You can improve your answer by seeing what this link finds you and providing a brief summary of the material you found, similar to the other answer.
$endgroup$
– Discrete lizard
Jul 3 at 8:52
add a comment |
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2 Answers
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active
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2 Answers
2
active
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active
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active
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$begingroup$
There are a few courses on Coursera that offer such learning materials.
Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming (Intermediate)
The primary topics in this part of the specialization are: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, optimal search trees).
If you want to go directly to dynamic programming then you can skip to weeks 3 and 4 of the syllabus.
Practical Reinforcement Learning (Advanced)
Here you will find out about:
foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc. - with math & batteries included
using deep neural networks for RL tasks - also known as "the hype train"
state of the art RL algorithms - and how to apply duct tape to them for practical problems.
and, of course, teaching your neural network to play games - because that's what everyone thinks RL is about. We'll also use it for seq2seq and contextual bandits.
$endgroup$
add a comment |
$begingroup$
There are a few courses on Coursera that offer such learning materials.
Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming (Intermediate)
The primary topics in this part of the specialization are: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, optimal search trees).
If you want to go directly to dynamic programming then you can skip to weeks 3 and 4 of the syllabus.
Practical Reinforcement Learning (Advanced)
Here you will find out about:
foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc. - with math & batteries included
using deep neural networks for RL tasks - also known as "the hype train"
state of the art RL algorithms - and how to apply duct tape to them for practical problems.
and, of course, teaching your neural network to play games - because that's what everyone thinks RL is about. We'll also use it for seq2seq and contextual bandits.
$endgroup$
add a comment |
$begingroup$
There are a few courses on Coursera that offer such learning materials.
Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming (Intermediate)
The primary topics in this part of the specialization are: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, optimal search trees).
If you want to go directly to dynamic programming then you can skip to weeks 3 and 4 of the syllabus.
Practical Reinforcement Learning (Advanced)
Here you will find out about:
foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc. - with math & batteries included
using deep neural networks for RL tasks - also known as "the hype train"
state of the art RL algorithms - and how to apply duct tape to them for practical problems.
and, of course, teaching your neural network to play games - because that's what everyone thinks RL is about. We'll also use it for seq2seq and contextual bandits.
$endgroup$
There are a few courses on Coursera that offer such learning materials.
Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming (Intermediate)
The primary topics in this part of the specialization are: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, optimal search trees).
If you want to go directly to dynamic programming then you can skip to weeks 3 and 4 of the syllabus.
Practical Reinforcement Learning (Advanced)
Here you will find out about:
foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc. - with math & batteries included
using deep neural networks for RL tasks - also known as "the hype train"
state of the art RL algorithms - and how to apply duct tape to them for practical problems.
and, of course, teaching your neural network to play games - because that's what everyone thinks RL is about. We'll also use it for seq2seq and contextual bandits.
answered Jun 30 at 10:06
TheSimpliFireTheSimpliFire
8893 silver badges25 bronze badges
8893 silver badges25 bronze badges
add a comment |
add a comment |
$begingroup$
AFAIK, There are some examples on the YouTube host. For instance, this link.
$endgroup$
$begingroup$
This is a link to a search term of the main words in the title. While this could be a decent suggestion, I don't think this is a proper answer. You can improve your answer by seeing what this link finds you and providing a brief summary of the material you found, similar to the other answer.
$endgroup$
– Discrete lizard
Jul 3 at 8:52
add a comment |
$begingroup$
AFAIK, There are some examples on the YouTube host. For instance, this link.
$endgroup$
$begingroup$
This is a link to a search term of the main words in the title. While this could be a decent suggestion, I don't think this is a proper answer. You can improve your answer by seeing what this link finds you and providing a brief summary of the material you found, similar to the other answer.
$endgroup$
– Discrete lizard
Jul 3 at 8:52
add a comment |
$begingroup$
AFAIK, There are some examples on the YouTube host. For instance, this link.
$endgroup$
AFAIK, There are some examples on the YouTube host. For instance, this link.
answered Jul 2 at 8:18
abbas omidiabbas omidi
3235 bronze badges
3235 bronze badges
$begingroup$
This is a link to a search term of the main words in the title. While this could be a decent suggestion, I don't think this is a proper answer. You can improve your answer by seeing what this link finds you and providing a brief summary of the material you found, similar to the other answer.
$endgroup$
– Discrete lizard
Jul 3 at 8:52
add a comment |
$begingroup$
This is a link to a search term of the main words in the title. While this could be a decent suggestion, I don't think this is a proper answer. You can improve your answer by seeing what this link finds you and providing a brief summary of the material you found, similar to the other answer.
$endgroup$
– Discrete lizard
Jul 3 at 8:52
$begingroup$
This is a link to a search term of the main words in the title. While this could be a decent suggestion, I don't think this is a proper answer. You can improve your answer by seeing what this link finds you and providing a brief summary of the material you found, similar to the other answer.
$endgroup$
– Discrete lizard
Jul 3 at 8:52
$begingroup$
This is a link to a search term of the main words in the title. While this could be a decent suggestion, I don't think this is a proper answer. You can improve your answer by seeing what this link finds you and providing a brief summary of the material you found, similar to the other answer.
$endgroup$
– Discrete lizard
Jul 3 at 8:52
add a comment |
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