26
votes
Accepted
What are the tradeoffs between "exact" and Reinforcement Learning methods for solving optimization problems
As far as I understand it, all machine learning approaches used for solving (combinatorial) optimization problems, and in particular reinforcement learning, work as follows:
Use a greedy algorithm to ...
- 5,909
24
votes
Accepted
Deep Reinforcement Learning for General Purpose Optimization
"General purpose optimization" is quite broad, so I'll take a step back first, to better identifying the motivation of using ML in optimization settings.
To keep things simple, I'll consider ...
- 4,068
17
votes
Accepted
What is the connection of Operations Research and Reinforcement Learning?
In the operations research community, many study dynamic programming models and solution approaches. Since many interesting dynamic programming problems attempt to model information uncertainty over ...
- 1,542
15
votes
What are the tradeoffs between "exact" and Reinforcement Learning methods for solving optimization problems
Reinforcement learning is set of the algorithms which are used to solve Markov Decision Processes and its variants, e.g. Partially Observed MDP (POMDP).
Most of the problems that we deal with them ...
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11
votes
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Online Education for OR and Developing Decision Support Systems
Check Coursera, edX, Udemy, or any other online courses (such as those of Stanford). For example:
Free: Discrete Optimization course on Coursera, covers column generation and an introduction to (meta)...
- 5,864
11
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What is the connection of Operations Research and Reinforcement Learning?
RL (and Machine Learning in general) often use specialized forms of continuous optimization problems that enable one to use elegant solving techniques (e.g. training/learning on the GPU or even ...
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11
votes
Suggestion of some courses in sequential decision making
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 ...
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9
votes
Accepted
Status of reinforcement learning for (mixed) integer programming?
Firstly, be ware that "traditional approaches" do not need to be exact. Heuristics are a big thing in OR for decades.
Now to answer your question: Reinforcement learning is not state-of-the-...
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8
votes
What are the tradeoffs between "exact" and Reinforcement Learning methods for solving optimization problems
A main reason to use Reinforced Learning (RL) is because you don't know the dynamics (update rules) of the system.
If you don't know the details of how the system will update, you will not be able to ...
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7
votes
What is the connection of Operations Research and Reinforcement Learning?
Something that I think is becoming more prominent is the use of RL for what would have traditionally been solved using exact solutions.
https://medium.com/octavian-ai/finding-shortest-paths-with-...
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6
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Online Education for OR and Developing Decision Support Systems
For heuristics you can take a look at this course. Also the book "How to solve it : Modern heuristics" by Zbigniew Michalewicz and David B. Fogel.
Reinforcement Learning has similiarities with ...
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5
votes
Online Education for OR and Developing Decision Support Systems
Institute of Applied Optimization
Metaheuristic Optimization - prerequisite: Java programming
Coursera
Practical Reinforcement Learning - related
- 5,341
4
votes
Online Education for OR and Developing Decision Support Systems
For Reinforcement learning and Deep Reinforcement learning, a wonderful online free course on RL by David Silver (the first author of the AlphaZero, AlphaGo algorithms) exists:
YouTube links
A more ...
- 825
4
votes
Suggestion of some courses in sequential decision making
The previous answer provided a great list on the classical dynamic programming. For Reinforcement learning and Deep Reinforcement learning, a wonderful online free course on RL by David Silver (the ...
- 825
3
votes
What are the tradeoffs between "exact" and Reinforcement Learning methods for solving optimization problems
Reinforcement learning does not seem to be the most obvious choice in machine learning for optimisation. I have heard a lot of using supervised learning to decide the decisions to take when exploring ...
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3
votes
Status of reinforcement learning for (mixed) integer programming?
The combination of "traditional approaches" with, for example, RL-based approaches may be the state-of-the-art solution for the MIP (this reference as an instance, in which the RL help for ...
- 31
1
vote
When was the exploration and exploitation tradeoff first mentioned in literature?
With reference to the paper in the original post "Exploration and Exploitation in Organizational Learning" (E&E) by James G. March, the paper "The Lost Experiment in Exploration and ...
- 131
1
vote
Accepted
Determining which choice strategy (epsilon-greedy, softmax, UCB, etc.) fits participant behavior in a multi armed bandit study
Figured it out. It works exactly they way I originally intended to do it in my question. See this tutorial for detailed information on how to actually implement the procedure with real data.
- 31
1
vote
Accepted
Understanding MDP's Dual Linear Program
It is just $$\sum_{n=1}^\infty a_n = \sum_{n=0}^\infty a_n - a_0 = \sum_{n=1}^\infty a_{n-1} - a_0,$$
where
$$a_n=\lambda^n P^{d^\infty}(X_{n+1}=j\mid X_1=k).$$
In particular
$$a_0=\lambda^0 P^{d^\...
- 30.5k
1
vote
Can we use reinforcement learning and convex optimization to solve an optimization problem?
You can use RL in any step. But problem is optimality check of solution which is explained above. Also you can solve your problem directly using RL such as RL for VRP. And you can read this blog ...
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vote
Can we use reinforcement learning and convex optimization to solve an optimization problem?
Not really, but approximately. By OR standards, a problem is "solved" once we manage to satisfy the KKT conditions. There is no machine learning algorithm to date that can consistently ...
- 11.9k
1
vote
Accepted
Can Deep RL be used to find optimal division point in an application?
This is a clustering problem, i.e., you want split your services into two clusters, each of which will run on a different device.
The way you would do this with RL is to use the goals an objective ...
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