Hi, I’m working on a reinforcement learning project to teach an AI to play a video game. Specifically I’m implementing A2C. The RL literature has many examples of either continuous or discrete action spaces but many video games have both types of inputs e.g. mouse position and keyboard input.

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In my specific scenario I have continuous mouse input: X and Y coordinate. A set of 5 weapons the agent chooses 1 from. And 5 buttons the agent can press (for jumping, shooting, etc.) which can be pressed simultaneously.

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Can I have multiple output heads for the different types of actions and take the mean of the losses? Will it converge?

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Specifically there would be two heads with two nodes each for mouse position mean and variance. Additionally one head with 5 nodes for the weapon selection with softmax activation. And another head with 5 nodes for the “button actions” with sigmoid activation.

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Of course you need different loss and entropy functions for the continuous mouse position and the discrete weapon selection. I don’t know how to calculate loss and entropy of my “button actions”, but that’s another question: https://www.reddit.com/r/MachineLearning/comments/9z8tok/d_reinforcement_learning_with_multiple/

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So if I have the loss and entropy for each of mouse position, weapon selection and “button actions”. Should I average all the losses and all the entropies to use them in my final loss function? Should they be weighted in some way?

Hello people. I work with large Lightroom catalogs with hundreds of pictures that I have to sift through and was thinking of a way to automate this job to a certain degree.

So here comes the big question. Is there a way of training a model to select pictures based on facial expression? Basically selecting the best pictures where people are smiling and looking their best. I could feed it a lot of examples of what I consider good pictures. I imagine something this specific might not exist yet, but can someone point me to a good starting point for this?

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Thank you!

Hi, I am currently a master student at GaTech. I was a game developer after I graduate from Pitt in Computer engineering. I am completely new to this realm. So, could you guys recommend some classes? There are just too many of them. And I only have eight classes left to finish.

Here is the course list. Classes that are in bold text are what I selected.

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Algorithm(Pick one or more):

• Computability, Algorithms, and Complexity
• Computational Complexity Theory
• Design and Analysis of Algorithms
• Graph Algorithms
• Approximation Algorithms
• Randomized Algorithms
• Computational Science and Engineering Algorithms

Core(Pick one or more):

• Machine Learning
• Computational Data Analysis: Learning, Mining, and Computation

Elective(Pick three or more):

• Big Data Systems & Analysis
• Computer Vision
• Markov Chain Monte Carlo
• Spectral Algorithms
• Machine Learning Theory
• Pattern Recognition
• Behavioral Imaging
• Deep Learning
• Machine Learning for Trading
• Natural Language
• Web Search and Text Mining
• Data and Visual Analytics
• Computational Statistics
• Bayesian Methods
• Stochastic Optimization

Special Topics(Pick Less than four):

• Interactive Robot Learning
• ML with Limited Supervision
• Math Foundations Mach Learning

Abstract An unsolved problem in neuroevolution (NE) is to evolve artificial neural networks (ANN) that can store and use information to change their behavior online. While plastic neural networks have shown promise in this context, they have difficulties retaining information over longer periods of time and integrating new information without losing previously acquired skills. Here we build on recent work by Graves et al. who extended the capabilities of an ANN by combining it with an external memory bank trained through gradient descent. In this paper, we introduce an evolvable version of their Neural Turing Machine (NTM) and show that such an approach greatly simplifies the neural model, generalizes better, and does not require accessing the entire memory content at each time-step. The Evolvable Neural Turing Machine (ENTM) is able to solve a simple copy tasks and for the first time, the continuous version of the double T-Maze, a complex reinforcement-like learning problem. In the T-Maze learning task the agent uses the memory bank to display adaptive behavior that normally requires a plastic ANN, thereby suggesting a complementary and effective mechanism for adaptive behavior in NE.

pdf

https://dl.acm.org/citation.cfm?id=2908930

So basically what i have is couple of colab notebooks that do what i want, and do that with their free gpu… for free(which is required). What i want to do is setup an apache airflow scheduler with some kind of flask app that would take my request, schedule it for execution and run for example notebook A with passed arguments/files and return back files aswell. Im thinking off two solutions. One would be using some kind of google cloud run? Do they offer free tier gpus aswell? the other solution that comes to my mind is spawning free tier google compute server and doing that this way(dockerizing my notebooks, sending files to google storage running docker and saving to cloud storage and than retrieving the results). Im really lost when it comes to google cloud offerings for dl. Notebooks do said things just fine but i want to schedule them so they could run my models continously.

So quick recap, what i want:

– scheduler schedules and runs a job(sends files to endpoint)

– X runs long running model training on free tier gpu

– scheduler gets back the resulting file

What do you guys think? What would be the best way to do this?

Thanks!