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A Beginner's Guide to Deep Reinforcement Learning

When it is not in our power to determine what is true, we ought to act in accordance with what is most probable.

- Descartes Contents Deep reinforcement learning combines artificial neural networks with a reinforcement learning architecture that enables software-defined agents to learn the best actions possible in virtual environment in order to attain their goals.

That’s a mouthful, but all will be explained below, in greater depth and plainer language, drawing (surprisingly) from your personal experiences as a person moving through the world.

While neural networks are responsible for recent AI breakthroughs in problems like computer vision, machine translation and time series prediction – they can also combine with reinforcement learning algorithms to create something astounding like Deepmind’s AlphaGo, an algorithm that beat the world champions of the Go board game.

Reinforcement algorithms that incorporate deep neural networks can beat human experts playing numerous Atari video games, Starcraft II and Dota-2, as well as the world champions of Go.

It’s reasonable to assume that reinforcement learning algorithms will slowly perform better and better in more ambiguous, real-life environments while choosing from an arbitrary number of possible actions, rather than from the limited options of a repeatable video game.

Pathmind applies deep reinforcement learning to simulations of real-world use cases to help businesses optimize how they build factories, staff call centers, set up warehouses and supply chains, and manage traffic flows.

Barto In the feedback loop above, the subscripts denote the time steps t and t+1, each of which refer to different states: the state at moment t, and the state at moment t+1.

Unlike other forms of machine learning – such as supervised and unsupervised learning – reinforcement learning can only be thought about sequentially in terms of state-action pairs that occur one after the other.

We can illustrate their difference by describing what they learn about a “thing.” One way to imagine an autonomous reinforcement learning agent would be as a blind person attempting to navigate the world with only their ears and a white cane.

The goal of reinforcement learning is to pick the best known action for any given state, which means the actions have to be ranked, and assigned values relative to one another.

Here are a few examples to demonstrate that the value and meaning of an action is contingent upon the state in which it is taken: We map state-action pairs to the values we expect them to produce with the Q function, described above.

Reinforcement learning is the process of running the agent through sequences of state-action pairs, observing the rewards that result, and adapting the predictions of the Q function to those rewards until it accurately predicts the best path for the agent to take.

After a little time spent employing something like a Markov decision process to approximate the probability distribution of reward over state-action pairs, a reinforcement learning algorithm may tend to repeat actions that lead to reward and cease to test alternatives.

Just as oil companies have the dual function of pumping crude out of known oil fields while drilling for new reserves, so too, reinforcement learning algorithms can be made to both exploit and explore to varying degrees, in order to ensure that they don’t pass over rewarding actions at the expense of known winners.

Indeed, the true advantage of these algorithms over humans stems not so much from their inherent nature, but from their ability to live in parallel on many chips at once, to train night and day without fatigue, and therefore to learn more.

Rather than use a lookup table to store, index and update all possible states and their values, which impossible with very large problems, we can train a neural network on samples from the state or action space to learn to predict how valuable those are relative to our target in reinforcement learning.

Like all neural networks, they use coefficients to approximate the function relating inputs to outputs, and their learning consists to finding the right coefficients, or weights, by iteratively adjusting those weights along gradients that promise less error.

Using feedback from the environment, the neural net can use the difference between its expected reward and the ground-truth reward to adjust its weights and improve its interpretation of state-action pairs.

This leads us to a more complete expression of the Q function, which takes into account not only the immediate rewards produced by an action, but also the delayed rewards that may be returned several time steps deeper in the sequence.

Just as calling the wetware method human() contains within it another method human(), of which we are all the fruit, calling the Q function on a given state-action pair requires us to call a nested Q function to predict the value of the next state, which in turn depends on the Q function of the state after that, and so forth.

One action screen might be “jump harder from this state”, another might be “run faster in this state” and so on and so forth.) Since some state-action pairs lead to significantly more reward than others, and different kinds of actions such as jumping, squatting or running can be taken, the probability distribution of reward over actions is not a bell curve but instead complex, which is why Markov and Monte Carlo techniques are used to explore it, much as Stan Ulam explored winning Solitaire hands.

AI think tank OpenAI trained an algorithm to play the popular multi-player video game Data 2 for 10 months, and every day the algorithm played the equivalent of 180 years worth of games.

Just as knowledge from the algorithm’s runs through the game is collected in the algorithm’s model of the world, the individual humans of any group will report back via language, allowing the collective’s model of the world, embodied in its texts, records and oral traditions, to become more intelligent (At least in the ideal case.

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