When it is not in our power to determine what is true, we ought to act in accordance with what is most probable. - Descartes
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 is, it unites function approximation and target optimization, mapping state-action pairs to expected rewards.
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 learning refers to goal-oriented algorithms, which learn how to attain a complex objective (goal) or how to maximize along a particular dimension over many steps; for example, they can maximize the points won in a game over many moves. RL algorithms can start from a blank slate, and under the right conditions, they achieve superhuman performance. Like a pet incentivized by scolding and treats, these algorithms are penalized when they make the wrong decisions and rewarded when they make the right ones – this is reinforcement.
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. While that may sound trivial to non-gamers, it’s a vast improvement over reinforcement learning’s previous accomplishments, and the state of the art is progressing rapidly.
Reinforcement learning solves the difficult problem of correlating immediate actions with the delayed returns they produce. Like humans, reinforcement learning algorithms sometimes have to wait a while to see the fruit of their decisions. They operate in a delayed return environment, where it can be difficult to understand which action leads to which outcome over many time steps.
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. That is, with time we expect them to be valuable to achieve goals in the real world. They may even be the most promising path to strong AI, given sufficient data and compute.
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.
Reinforcement learning can be understood using the concepts of agents, environments, states, actions and rewards, all of which we’ll explain below. Capital letters tend to denote sets of things, and lower-case letters denote a specific instance of that thing; e.g.
A is all possible actions, while
a is a specific action contained in the set.
Ais the set of all possible moves the agent can make. An action is almost self-explanatory, but it should be noted that agents usually choose from a list of discrete, possible actions. In video games, the list might include running right or left, jumping high or low, crouching or standing still. In the stock markets, the list might include buying, selling or holding any one of an array of securities and their derivatives. When handling aerial drones, alternatives would include many different velocities and accelerations in 3D space.
0.8³ x 10. A discount factor of 1 would make future rewards worth just as much as immediate rewards. We’re fighting against delayed gratification here.
Vπ(s)is defined as the expected long-term return of the current state under policy
π. We discount rewards, or lower their estimated value, the further into the future they occur. See discount factor. And remember Keynes: “In the long run, we are all dead.” That’s why you discount future rewards. It is useful to distinguish
Qπ(s, a)refers to the long-term return of an action taking action a under policy
πfrom the current state
s. Q maps state-action pairs to rewards. Note the difference between Q and policy.
So environments are functions that transform an action taken in the current state into the next state and a reward; agents are functions that transform the new state and reward into the next action. We can know and set the agent’s function, but in most situations where it is useful and interesting to apply reinforcement learning, we do not know the function of the environment. It is a black box where we only see the inputs and outputs. It’s like most people’s relationship with technology: we know what it does, but we don’t know how it works. Reinforcement learning represents an agent’s attempt to approximate the environment’s function, such that we can send actions into the black-box environment that maximize the rewards it spits out.
*Credit: Sutton & Barto
In the feedback loop above, the subscripts denote the time steps
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.
Reinforcement learning judges actions by the results they produce. It is goal oriented, and its aim is to learn sequences of actions that will lead an agent to achieve its goal, or maximize its objective function. Here are some examples:
Here’s an example of an objective function for reinforcement learning; i.e. the way it defines its goal.
We are summing reward function r over t, which stands for time steps. So this objective function calculates all the reward we could obtain by running through, say, a game. Here, x is the state at a given time step, and a is the action taken in that state. r is the reward function for x and a. (We’ll ignore γ for now.)
Reinforcement learning differs from both supervised and unsupervised learning by how it interprets inputs. 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. Agents have small windows that allow them to perceive their environment, and those windows may not even be the most appropriate way for them to perceive what’s around them.
Automatically apply RL to simulation use cases (e.g. call centers, warehousing, etc.) using Pathmind.Get Started
(In fact, deciding which types of input and feedback your agent should pay attention to is a hard problem to solve. This is known as domain selection. Algorithms that are learning how to play video games can mostly ignore this problem, since the environment is man-made and strictly limited. Thus, video games provide the sterile environment of the lab, where ideas about reinforcement learning can be tested. Domain selection requires human decisions, usually based on knowledge or theories about the problem to be solved; e.g. selecting the domain of input for an algorithm in a self-driving car might include choosing to include radar sensors in addition to cameras and GPS data.)
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. Since those actions are state-dependent, what we are really gauging is the value of state-action pairs; i.e. an action taken from a certain state, something you did somewhere. 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:
If the action is marrying someone, then marrying a 35-year-old when you’re 18 probably means something different than marrying a 35-year-old when you’re 90, and those two outcomes probably have different motivations and lead to different outcomes.
If the action is yelling “Fire!”, then performing the action a crowded theater should mean something different from performing the action next to a squad of men with rifles. We can’t predict an action’s outcome without knowing the context.
We map state-action pairs to the values we expect them to produce with the Q function, described above. The Q function takes as its input an agent’s state and action, and maps them to probable rewards.
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. That prediction is known as a policy.
Reinforcement learning is an attempt to model a complex probability distribution of rewards in relation to a very large number of state-action pairs. This is one reason reinforcement learning is paired with, say, a Markov decision process, a method to sample from a complex distribution to infer its properties. It closely resembles the problem that inspired Stan Ulam to invent the Monte Carlo method; namely, trying to infer the chances that a given hand of solitaire will turn out successful.
Any statistical approach is essentially a confession of ignorance. The immense complexity of some phenomena (biological, political, sociological, or related to board games) make it impossible to reason from first principles. The only way to study them is through statistics, measuring superficial events and attempting to establish correlations between them, even when we do not understand the mechanism by which they relate. Reinforcement learning, like deep neural networks, is one such strategy, relying on sampling to extract information from data.
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. There is a tension between the exploitation of known rewards, and continued exploration to discover new actions that also lead to victory. 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.
Reinforcement learning is iterative. In its most interesting applications, it doesn’t begin by knowing which rewards state-action pairs will produce. It learns those relations by running through states again and again, like athletes or musicians iterate through states in an attempt to improve their performance.
You could say that an algorithm is a method to more quickly aggregate the lessons of time.2 Reinforcement learning algorithms have a different relationship to time than humans do. An algorithm can run through the same states over and over again while experimenting with different actions, until it can infer which actions are best from which states. Effectively, algorithms enjoy their very own Groundhog Day, where they start out as dumb jerks and slowly get wise.
Since humans never experience Groundhog Day outside the movie, reinforcement learning algorithms have the potential to learn more, and better, than humans. 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. An algorithm trained on the game of Go, such as AlphaGo, will have played many more games of Go than any human could hope to complete in 100 lifetimes.3
Where do neural networks fit in?
Neural networks are function approximators, which are particularly useful in reinforcement learning when the state space or action space are too large to be completely known.
A neural network can be used to approximate a value function, or a policy function. That is, neural nets can learn to map states to values, or state-action pairs to Q values. 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.
In reinforcement learning, convolutional networks can be used to recognize an agent’s state when the input is visual; e.g. the screen that Mario is on, or the terrain before a drone. That is, they perform their typical task of image recognition.
But convolutional networks derive different interpretations from images in reinforcement learning than in supervised learning. In supervised learning, the network applies a label to an image; that is, it matches names to pixels.
In fact, it will rank the labels that best fit the image in terms of their probabilities. Shown an image of a donkey, it might decide the picture is 80% likely to be a donkey, 50% likely to be a horse, and 30% likely to be a dog.
In reinforcement learning, given an image that represents a state, a convolutional net can rank the actions possible to perform in that state; for example, it might predict that running right will return 5 points, jumping 7, and running left none.
The above image illustrates what a policy agent does, mapping a state to the best action.
A policy maps a state to an action.
If you recall, this is distinct from Q, which maps state action pairs to rewards.
To be more specific, Q maps state-action pairs to the highest combination of immediate reward with all future rewards that might be harvested by later actions in the trajectory. Here is the equation for Q, from Wikipedia:
Having assigned values to the expected rewards, the Q function simply selects the state-action pair with the highest so-called Q value.
At the beginning of reinforcement learning, the neural network coefficients may be initialized stochastically, or randomly. 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 feedback loop is analogous to the backpropagation of error in supervised learning. However, supervised learning begins with knowledge of the ground-truth labels the neural network is trying to predict. Its goal is to create a model that maps different images to their respective names.
Reinforcement learning relies on the environment to send it a scalar number in response to each new action. The rewards returned by the environment can be varied, delayed or affected by unknown variables, introducing noise to the feedback loop.
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.
Like human beings, the Q function is recursive. 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.
1) It might be helpful to imagine a reinforcement learning algorithm in action, to paint it visually. Let’s say the algorithm is learning to play the video game Super Mario. It’s trying to get Mario through the game and acquire the most points. To do that, we can spin up lots of different Marios in parallel and run them through the space of all possible game states. It’s as though you have 1,000 Marios all tunnelling through a mountain, and as they dig (e.g. as they decide again and again which action to take to affect the game environment), their experience-tunnels branch like the intricate and fractal twigs of a tree. The Marios’ experience-tunnels are corridors of light cutting through the mountain. And as in life itself, one successful action may make it more likely that successful action is possible in a larger decision flow, propelling the winning Marios onward. You might also imagine, if each Mario is an agent, that in front of him is a heat map tracking the rewards he can associate with state-action pairs. (Imagine each state-action pair as have its own screen overlayed with heat from yellow to red. The many screens are assembled in a grid, like you might see in front of a Wall St. trader with many monitors. 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. That is, while it is difficult to describe the reward distribution in a formula, it can be sampled. Because the algorithm starts ignorant and many of the paths through the game-state space are unexplored, the heat maps will reflect their lack of experience; i.e. there could be blanks in the heatmap of the rewards they imagine, or they might just start with some default assumptions about rewards that will be adjusted with experience. The Marios are essentially reward-seeking missiles guided by those heatmaps, and the more times they run through the game, the more accurate their heatmap of potential future reward becomes. The heatmaps are basically probability distributions of reward over the state-action pairs possible from the Mario’s current state.
2) Technology collapses time and space, what Joyce called the “ineluctable modalities of being.” What do we mean by collapse? Very long distances start to act like very short distances, and long periods are accelerated to become short periods. For example, radio waves enabled people to speak to others over long distances, as though they were in the same room. The same could be said of other wave lengths and more recently the video conference calls enabled by fiber optic cables. While distance has not been erased, it matters less for some activities. Any number of technologies are time savers. Household appliances are a good example of technologies that have made long tasks into short ones. But the same goes for computation. The rate of computational, or the velocity at which silicon can process information, has steadily increased. And that speed can be increased still further by parallelizing your compute; i.e. breaking up a computational workload and distributing it over multiple chips to be processed simultaneously. Parallelizing hardware is a way of parallelizing time. That’s particularly useful and relevant for algorithms that need to process very large datasets, and algorithms whose performance increases with their experience. 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. At the end of those 10 months, the algorithm (known as OpenAI Five) beat the world-champion human team. That victory was the result of parallelizing and accelerating time, so that the algorithm could leverage more experience than any single human could hope to collect, in order to win.
3) The correct analogy may actually be that a learning algorithm is like a species. Each simulation the algorithm runs as it learns could be considered an individual of the species. 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. The subversion and noise introduced into our collective models is a topic for another post, and probably for another website entirely.). This puts a finer point on why the contest between algorithms and individual humans, even when the humans are world champions, is unfair. We are pitting a civilization that has accumulated the wisdom of 10,000 lives against a single sack of flesh.
Reinforcement Learning Methods