As per this thread and this tweet, Open AI Five was trained on something like 45,000 years of gameplay experience, and it took less than one day for humans to figure out strategies to consistently beat it.

Open AI Five, together with AlphaStar, is the largest and most sophisiticated implementation of DRL, and yet it falls short of human intelligence by this huge margin. And I bet that AlphaStar would succumb to the same fate if they released it as a bot for anybody to play with.

I know there is lots of research going on to make DRL more data efficient, and to make deep learning in general more robust to out-of-distribution and adversarial examples, but the gap with humans here is so extreme that I doubt it can be meaningfully closed by anything short of a paradigm shift.

What are your thoughts? Is this the limit of what can be achieved by DRL, or is there still hope to push the paradigm foward?

As part of my B.Tech Project on modeling RL algorithms for job shop scheduling, I have written an environment which can be integrated with your RL model. Along with the environment, I have added a naive Q Learning model and Deep Q Network model for some number of machines and jobs.

Find the repository with the codes, features, and instruction for usage here

I hope this environment is found useful. Also, feel free to provide any suggestion and feedback about the same.

In this century, technology will give us AI which…

https://www.reddit.com/r/IAmA/comments/bghqn9/i_am_michio_kaku_physicist_futurist_and_author_of/ell96mt?utm_medium=android_app&utm_source=share

I’m trying to figure out how can weighted MMD from Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation (chapter 3. Weighted Maximum Mean Discrepancy) be adapted for InfoVAE: A Tutorial on Information Maximizing Variational Autoencoders.

First one is written in some “unbiased approximation to MMD with linear complexity” terms but the second one is written with “kernel embedding trick” terms.

I guess some info can be found in original papers: A Kernel Two-Sample Test (about terms from weighted MMD article) and A Kernel Method for the Two-Sample Problem (presumably original article on MMD) but that a bit too much for me.

I would appreciate any ideas on how to adapt this for InfoVAE.

All of the four terms (Artificial Intelligence, Machine Learning, Data Science and Big Data) are interlinked, but not interchangeable. I guess the following overview would help beginners with easily differentiating them: Everything you need to know about key differences between AI, Data Science, Machine Learning and Big Data (see the article for more details)

• AI focuses on mimicking decision-making processes.
• ML reaches beyond the available pieces of training information and interpret data that has never been encountered before.
• Data science combines various methods and data of diverse volumes in order to derive useful, mostly business-oriented, insights through both structural and predictive analyses.
• Big data doesn’t analyze but focuses on processing (with high velocity) extreme volumes and a wide variety of data types.

OG lost to the OpenAI Five bots team with a score of 0-2 in an exhibition match. On the second map, the International 2018 champions were defeated in less than 20 minutes.

Both teams picked heroes from a limited hero pool. Unlike previous show matches, OG was permitted to use invisibility, and the OpenAI Five courier was not invulnerable. At the same time, illusions and summoned unit were not allowed.

This is excellent news for AI enthusiasts. The state of AI development is getting better to the point that they could even defeat the champions of a complicated game.

In other news, the World Cyber Games has an upcoming AI robot football tournament. Interested programmers will have the chance to showcase their programming skills and compete against other AI robot programmers. More details about the AI competition here.

Watch the OG vs Open AI show match here: https://youtu.be/n8c4lOkgr_U

WCG AI Source > http://www.wcg.com/news/view/534

An an implementation of PlaNet written in PyTorch that supports gym environments and a few continuous control tasks.

GitHub repo: https://github.com/Kaixhin/PlaNet

Original implementation in TF: https://github.com/google-research/planet

Link to paper: https://planetrl.github.io