Name: TF-Agents.強化学習 (TensorFlow Meets) (TF-Agents: Reinforcement Learning (TensorFlow Meets))
Uploaded: 2021-01-14T10:37:34.000Z
Duration: 4 min 49 s
Description: VoiceTubeの動画で発音を聞きながら英語表現を覚えよう！学べる英語：

Hi everybody, and welcome to this episode of TensorFlow Meets.

I'm delighted to be chatting with Sergio Guadarrama.

You're from the TensorFlow Agents team, right?

Now, you did a talk at the TensorFlow Developer Summit

and could you tell us all about what TF Agents is and what it does?

So, TF Agents is a reinforcement learning library for TensorFlow

to solve many of the problems that we have.

We were struggling to get all these RL algorithms

So we decided to build this library with a lot of tests

Okay, so anybody can download TensorFlow Agents,

Now, you mentioned it's about reinforcement learning.

To most of us, we kind of know a little bit about reinforcement learning,

but could you tell us what it really is and what it's all about?

So, the main idea behind reinforcement learning

is like when you interact with its own environment,

you're going to play different actions and then you're going to get a reward

when you do the things correctly, and then you're going to get

a negative reward when you think the things incorrectly.

Basically, on that reward, you can learn.

So, almost like the way a real person learns.

It's kind of like a person learns when you get rewarded,

that you spoke about in your presentation--

and we have that on YouTube for people to watch--

but one of the things you spoke about that I thought was really cool,

where there's the wall, then there's the bat,

how does that work from a reinforcement learning perspective?

in this case, the game, see where the bricks are,

like where should I move the paddle-- to the left or to the right?

Eventually it will learn when you let it fall,

Right. Now, how does that work from a TF Agent's perspective.

Is the environment there, the game board or--

Yeah, it's already predefined, you can load it.

We have already a lot of environments defined for you,

so you can just load all the Atari games, OpenAI, Deep Mind Control,

But you can also define your own environment.

When you have a specific task, we make it very easy to bring it in

Now, in something like the Breakout game, for example,

So as you knock off a brick, your score goes up,

so how does it see that, how is it that getting labeled?

Is it reading the raw pixels on the screen,

So, in that case, it's actually given from the game.

In other cases, more complicated, like in a recommender system,

it would be based on the interaction with the user, for example.

I see. Okay, cool. Wow, interesting stuff.

Now, this is all open source that you said, right?

Now I noticed when I was poking around in there

Do you have any that you'd recommend people to play with, any favorites?

I think the best one to start is the DQN Cartpole.

It's a full example-- you can go through all the steps,

and you can see the videos, you can play around with it,

and how it solves to keep like a small cartpole,

Interesting. How long does it take to train that?

Wow, so reinforcement learning in a notebook with TF Agents,

and it takes a few minutes to help me predict a cartpole.

Yeah, the other thing is we are looking forward to the community

to contribute new environments, new tasks, or new algorithms

for people who have new ideas to contribute,

and we are looking forward to pull requests or GitHub issues.

Have you seen any scenarios that excite you?

Oh yeah, we applied it to some of the robotics tasks.

It was very interesting to see a robot actually learning how to grasp objects,

The first time you see it actually doing the task