What is reinforcement learning?
Reinforcement learning is the training of machine learning models to make a sequence of decisions. The agent learns to achieve a goal in an uncertain, potentially complex environment. In reinforcement learning, artificial intelligence faces a game-like situation. The computer employs trial and error to come up with a solution to the problem. To get the machine to do what the programmer wants, the artificial intelligence gets either rewards or penalties for the actions it performs. Its goal is to maximize the total reward.
Although the designer sets the reward policy–that is, the rules of the game–he gives the model no hints or suggestions for how to solve the game. It’s up to the model to figure out how to perform the task to maximize the reward, starting from totally random trials and finishing with sophisticated tactics and superhuman skills. By leveraging the power of search and many trials, reinforcement learning is currently the most effective way to hint machine’s creativity. In contrast to human beings, artificial intelligence can gather experience from thousands of parallel gameplays if a reinforcement learning algorithm is run on sufficiently powerful computer infrastructure.
What are the types of reinforcement learning?
Two kinds of reinforcement learning methods are:
It is defined as an event, that occurs because of specific behavior. It increases the strength and the frequency of the behavior and impacts positively on the action taken by the agent.
This type of Reinforcement helps you to maximize performance and sustain change for a more extended period. However, too much Reinforcement may lead to over-optimization of state, which can affect the results.
Negative Reinforcement is defined as the strengthening of behavior that occurs because of a negative condition that should have been stopped or avoided. It helps you to define the minimum stand of performance. However, the drawback of this method is that it provides enough to meet up the minimum behavior.
What are the advantages of reinforcement learning?
Reinforcement learning is the future of machine learning. Why? There are many situations where you just can’t label data effectively.
You have to learn from rewards and since supervised learning relies on large labeled datasets reinforcement learning has a much higher scope of application than any form of supervised learning.
Reinforcement learning doesn’t require large labeled datasets. It’s a massive advantage because as the amount of data in the world grows it becomes more and more costly to label it for all required applications.
It’s Innovative, unlike reinforcement learning supervised learning is actually imitating whoever provided the data for that algorithm.
The algorithm can learn to do the task as well or better than the teacher but can never learn a completely new approach to solving the problem.
On the other hand, reinforcement learning algorithms can come up with entirely new solutions that were never even considered by humans.
Goal-oriented, Reinforcement learning can be used for sequences of actions while supervised learning is mostly used in an input-output manner.
Reinforcement learning can be used for tasks with objectives such as robots playing soccer or self-driving cars getting to their destinations or an algorithm maximizing return on investment on ads spend.
Reinforcement learning is Adaptable, unlike supervised learning algorithms, reinforcement learning doesn’t require retraining because it adapts to new environments automatically on the fly.
How to implement reinforcement learning algorithms?
There are three approaches to implement a Reinforcement Learning algorithm.
In a value-based Reinforcement Learning method, you should try to maximize a value function V(s). In this method, the agent is expecting a long-term return of the current states under policy π.
In a policy-based RL method, you try to come up with such a policy that the action performed in every state helps you to gain maximum reward in the future.
Two types of policy-based methods are:
- Deterministic: For any state, the same action is produced by the policy π.
- Stochastic: Every action has a certain probability.
In this Reinforcement Learning method, you need to create a virtual model for each environment. The agent learns to perform in that specific environment.
What is the difference between reinforcement learning versus predictive analytics?
For all the hype, many organizations may soon come to realize that AI which promises “predictive analytics” fails to help them prepare for the future. That’s because it’s not really AI – it is a statistical analysis that goes back 200 years as linear regression was invented in 1805.
In fact, a recent research report from MIT showed the era of deep learning is ending, based on citations by other scientists. The same paper showed that machine learning and AI is really just statistics. Using statistics to primarily study historical data, does not offer a complete picture of how a system or business functions.
As the glut of deep learning experiments have run their course, the MIT researchers discovered, there has been a corresponding uptick in research on reinforcement learning.
Reinforcement learning delivers decisions. By creating a simulation of an entire business or system, it becomes possible for an intelligent system to test new actions or approaches, change course when failures happen (or negative reinforcement), while building on successes (or positive reinforcement).
Much in the way human beings can develop a skill as they practice it, reinforcement learning only becomes more powerful when it’s executed at scale.
What are the applications of reinforcement learning?
1. Self-driving cars
Various papers have proposed Deep Reinforcement Learning for autonomous driving. In self-driving cars, there are various aspects to consider, such as speed limits at various places, drivable zones, avoiding collisions — just to mention a few.
Some of the autonomous driving tasks where reinforcement learning could be applied include trajectory optimization, motion planning, dynamic pathing, controller optimization, and scenario-based learning policies for highways.
2. Industry automation
In industry reinforcement, learning-based robots are used to perform various tasks. Apart from the fact that these robots are more efficient than human beings, they can also perform tasks that would be dangerous for people. Centers can be fully controlled with the AI system without the need for human intervention. Of course, there should always be supervision from data center experts.
The system works in the following way:
- Taking snapshots of data from the data centers every five minutes and feeding this to deep neural networks
- It then predicts how different combinations will affect future energy consumptions
- Identifying actions that will lead to minimal power consumption while maintaining a set standard of safety criteria
- Sending and implement these actions at the data center
- The actions are verified by the local control system.
Supervised time series models can be used for predicting future sales as well as predicting stock prices. However, these models don’t determine the action to take at a particular stock price. Enter Reinforcement Learning (RL). An RL agent can decide on such a task; whether to hold, buy, or sell. The RL model is evaluated using market benchmark standards in order to ensure that it’s performing optimally.
4. Natural Language Processing
In NLP, RL can be used in text summarization, question answering, and machine translation just to mention a few.
In healthcare, patients can receive treatment from policies learned from RL systems. RL is able to find optimal policies using previous experiences without the need for previous information on the mathematical model of biological systems. It makes this approach more applicable than other control-based systems in healthcare.
Also read: Temporal difference learning