Computers that can play games have always impressed the computing world.
In December 2013, a small group of AI researchers from a London-based company called DeepMind
released a ground-breaking paper called “Playing Atari with Deep Reinforcement Learning”.
And just a little over a month later, Google announced that they had bought DeepMind for
a really big sum of money.
Since then, there’s been all kinds of talk about reinforcement learning in the field
of AI.
In January of 2016, Google announced that the appropriately named AlphaGo was able to
beat the reigning Go champion of the world.
We’re gonna take the mystery out of reinforcement learning so you can see how all these amazing
feats are possible.
The story of reinforcement Learning goes all the way back to AI, animal psychology, and
control theory.
At the heart of it, it involves an autonomous agent like a person, animal, robot, or deep
net - learning to navigate an uncertain environment with the goal of maximizing a numerical reward.
Sports are a great example of this.
Just think of what our autonomous agent would have to deal with in a tennis match.
The agent would have to consider its actions, like its serves, returns, and volleys.
These actions change the state of the game, or in other words – the current set, the
leading player, things like that.
And every action is performed with a reward in mind – winning a point, in order to win
the game, set, and match.
Our agent needs to follow a policy, or a set of rules and strategies, in order to maximize
the final score.
But if you were building an autonomous agent, how would you actually model this?
We know that the agent’s actions will change the state of the environment.
So a model would need to be able to take a state and an action as input, and generate
the maximum expected reward as output.
But since that only gets you to the next state, you’ll need to take into account the total
expected reward for every action from the current till the end state.
The way this works will be different for every application, and you’re probably not surprised
to know that building a Tennis agent is different from building an Atari agent.
The researchers at DeepMind used a series of Atari screenshots to build a convolutional
neural network, with a couple of tweaks.
The output wasn’t a class, but instead it was a target number for the maximum reward.
So it was actually dealing with regression, not classification.
They also didn’t use pooling layers, since unlike image recognition, individual positions
of game objects, like the player, are all important and can’t be reduced.
A recurrent net could have been used too, as long as the output layer was tailored for
regression, and the input at each time step included the action and the environment state.
There’s also the Deep Q-Network, or DQN for short.
The DQN also uses the principle of predicting the maximum reward given a state and action.
It was actually patented by Google, and it’s seen a lot of improvements like the Experience
Replay and the Dueling Network Architecture.
Reinforcement learning isn’t just a fancy, smart-sounding way to say supervised learning.
Supervised learning is all about making sense of the environment based on historical examples.
But that isn’t always the best way to do things.
Imagine if you’re trying to drive a car in heavy traffic based on the road patterns
you observed the week before when the roads were clear.
That’s about as effective as driving when you’re only looking at the rear view mirror.
Reinforcement learning on the other hand is all about reward.
You get points for your actions – like staying in your lane, driving under the speed limit,
signaling when you’re supposed to, things like that.
But you can also lose points for dangerous actions like tailgating and speeding.
Your objective is to get the maximum number of points possible given the current state
of the traffic on the road around you.
Reinforcement learning emphasizes that an action results in a change of the state, which
is something a supervised learning model doesn’t focus on.
In April of 2016, Amazon founder Jeff Bezos talked about how his company is a great place
to fail, and how most companies are unwilling to suffer through “the string of failed
experiments”.
You can think of this as a statement about rewards.
Most organizations operate in the realm of conventional wisdom, which is about exploiting
what is known to achieve finite rewards with known odds.
Some groups venture into the unknown and explore new territory with the prospect of out-sized
rewards at long odds.
And many of these organizations do fail!
But some of them succeed and end up changing the world.
With reinforcement learning, an agent can explore the trade-off between exploration
and exploitation, and choose the path to the maximum expected reward.
This channel’s all about Deep Learning, so we focused on the topic of building a deep
reinforcement net.
But reinforcement learning falls under the broader umbrella of artificial intelligence.
It involves topics like goal setting, planning, and perception.
And it can even form a bridge between AI and the engineering disciplines.
Reinforcement learning is simple and powerful, and given the recent advances, it has the
potential to become a big force in the field of Deep Learning.
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