Google's AI Plays Football…For Science! ⚽️

Dear Fellow Scholars, this is Two Minute Papers with Károly Zsolnai-Fehér.

Reinforcement learning is an important subfield within machine learning research where we

teach an agent to choose a set of actions in an environment to maximize a score.

This enables these AIs to play Atari games at a superhuman level, control drones, robot

arms, or even create self-driving cars.

A few episodes ago, we talked about DeepMind’s behavior suite that opened up the possibility

of measuring how these AIs perform with respect to the 7 core capabilities of reinforcement

learning algorithms.

Among them were how well such an AI performs when being shown a new problem, how well or

how much they memorize, how willing they are to explore novel solutions, how well they

scale to larger problems, and more.

In the meantime, the Google Brain research team has also been busy creating a physics-based

3D football, or for some of you, soccer simulation where we can ask an AI to control one, or

multiple players in this virtual environment.

This is a particularly difficult task because it requires finding a delicate balance between

rudimentary short-term control tasks, like passing, and long-term strategic planning.

In this environment, we can also test our reinforcement learning agents against handcrafted,

rule-based teams.

For instance, here you can see that DeepMind’s Impala algorithm is the only one that can

reliably beat the medium and hard handcrafted teams, specifically, the one that was run

for 500 million training steps.

The easy case is tuned to be suitable for single-machine research works, where the hard

case is meant to challenge sophisticated AIs that were trained on a massive array of machines.

I like this idea a lot.

Another design decision I particularly like here is that these agents can be trained from

pixels or internal game state.

Okay, so what does that really mean?

Training from pixels is easy to understand but very hard to perform - this simply means

that the agents see the same content as what we see on the screen.

DeepMind’s Deep Reinforcement Learning is able to do this by training a neural network

to understand what events take place on the screen, and passes, no pun intended all this

event information to a reinforcement learner that is responsible for the strategic, gameplay-related

decisions.

Now, what about the other one?

The internal game state learning means that the algorithm sees a bunch of numbers which

relate to quantities within the game, such as the position of all the players and the

ball, the current score and so on.

This is typically easier to perform because the AI is given high-quality and relevant

information and is not burdened with the task of visually parsing the entire scene.

For instance, OpenAI’s amazing DOTA2 team learned this way.

Of course, to maximize impact, the source code for this project is also available.

This will not only help researchers to train and test their own reinforcement learning

algorithms on a challenging scenario, but they can extend it and make up their own scenarios.

Now note that so far, I tried my hardest not to comment on the names of the players and

the teams, but my will to resist just ran out.

Go real Bayesians!

Thanks for watching and for your generous support, and I'll see you next time!