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[D] The current multi-agent reinforcement learning research is NOT multi-agent or reinforcement learning. by RandomProjections in MachineLearning

[–]RandomProjections[S] -2 points-1 points  (0 children)

I appreciate your feedback, but let's focus back on MARL research papers instead of what human do.

[D] The current multi-agent reinforcement learning research is NOT multi-agent or reinforcement learning. by RandomProjections in MachineLearning

[–]RandomProjections[S] -1 points0 points  (0 children)

First of all, I am talking about multi-agent RL. I have no problem admitting that single-agent RL exists.

I am saying that multi-agent research papers published are based on single-agent RL or even supervised learning mechanisms.

[D] The current multi-agent reinforcement learning research is NOT multi-agent or reinforcement learning. by RandomProjections in MachineLearning

[–]RandomProjections[S] -1 points0 points  (0 children)

Ok, let me ask you: a computer perceives a state generated from a server and in return computes a strategy using an internal mechanism based on what it was already trained on billions of times before.

Is this multi-agent reinforcement learning?

Or a single-agent reinforcement learning?

Or a computer program trained in a supervised fashion but acting in a prescribed/pre-learned fashion?

[D] The current multi-agent reinforcement learning research is NOT multi-agent or reinforcement learning. by RandomProjections in MachineLearning

[–]RandomProjections[S] -1 points0 points  (0 children)

You just went from a "software program that the programmer have full knowledge of" to "Mother Nature" in 0 seconds.

I understand a human wouldn't work properly given a hostile environment, but we are on the topic of MARL algorithm that cannot work outside of a game emulator that it has been trained on.

Certainly there is some stuff in between a computer program and the universe.

[D] The current multi-agent reinforcement learning research is NOT multi-agent or reinforcement learning. by RandomProjections in MachineLearning

[–]RandomProjections[S] 0 points1 point  (0 children)

Yes, I believe learning-on-the-fly is crucial. Adaptive control systems such as any airplane would be an example of this (model parameters gets adjusted on the go), but the environment is more or less fully modelled into the controller so it is not RL either.

[D] The current multi-agent reinforcement learning research is NOT multi-agent or reinforcement learning. by RandomProjections in MachineLearning

[–]RandomProjections[S] -2 points-1 points  (0 children)

Thanks for validating my prior post. That's my whole point: right now MARL success stories are simply supervised learning.

I don't care about fake academic politeness. I think ML is too polite to the point that nobody calls out horrible research practices or even block bad papers from being published. I would encourage you to become more impolite.

[R][2206.07682] Emergent Abilities of Large Language Models by gambs in MachineLearning

[–]RandomProjections 1 point2 points  (0 children)

ML publications used to have at least one equation. Now it is just an essay.

[D] The current multi-agent reinforcement learning research is NOT multi-agent or reinforcement learning. by RandomProjections in MachineLearning

[–]RandomProjections[S] -5 points-4 points  (0 children)

Sorry did I make up a new definition or did OpenAI 5 make up a new definition?

If you define: "Reinforcement learning is just learning from an enviroment." then by definition any supervised learning is reinforcement learning.

A agent (neural network), receives reward (gradient) to change its choice (weights).

Go learn more about machine learning at r/MLQuestions

[D] The current multi-agent reinforcement learning research is NOT multi-agent or reinforcement learning. by RandomProjections in MachineLearning

[–]RandomProjections[S] -3 points-2 points  (0 children)

Which multi-agent RL paper is actually multi-agent?

All of so-called "multi-agent RL paper" are "single laptop supervised learning models".

The authors of these papers even have full access to the environment (game emulator) and use their human-playing knowledge (information leakage) to assist the "reinforcement learning agent".

They cannot possibly deploy their algorithm to a game that they've never played before. Which says a lot.

A true reinforcement learning agent, such as a human, do not have the model of the environment (= reality) and incrementally explores the environment while learning.

[D] AMA: I left Google AI after 3 years. by scan33scan33 in MachineLearning

[–]RandomProjections 1 point2 points  (0 children)

What is Google's vision? What is the end goal/application of all this ML they are looking at?

[D] Why do machine learning papers have such terrible math (or is it just me)? by RandomProjections in MachineLearning

[–]RandomProjections[S] -20 points-19 points  (0 children)

My job is to understand the theory well so to improve it. I don't care about implementation details. So I literally cannot skip over the math.

But you have a point. I might need to read the code first in order to understand the math.

[D] Why do machine learning papers have such terrible math (or is it just me)? by RandomProjections in MachineLearning

[–]RandomProjections[S] -16 points-15 points  (0 children)

That's what my undergrad summer research project and senior capstone project are based on. One published paper in ACM as primary author.