How rewards create intelligent machines
In this article, we are going to discuss how reinforcement learning algorithms work? so let’s start with an introduction to reinforcement learning. In June 2021, researchers from the DeepMind AI lab made a contentious claim. The researchers proposed that artificial general intelligence (AGI) might be achieved using a single method that is reinforcement learning.
“Reward is Enough,” they titled their paper on the issue. The researchers proposed that AGI may evolve as a result of a reward function, which is a type of incentive mechanism. The study’s authors concluded, “We hypothesize that intelligence, and its associated abilities, can be interpreted as assisting the maximization of enjoyment.”
Their claims have been dismissed by some scientists, but they nonetheless shine a spotlight on a powerful technique.
What is reinforcement learning, and how does it work?
Reinforcement Learning Definition
A software agent learns through trial and error in reinforcement learning (RL). The model is rewarded when it performs the desired activity. Over time, the agent figures out the best way to do the mission in order to maximize its reward.
The method can be used for a variety of tasks, including controlling driverless cars and increasing energy efficiency. The company’s most well-known accomplishments, however, have been in the gaming industry. The technique reached a watershed moment in March 2016.
AlphaGo, a DeepMind system, became the first computer program to beat a world champion in Go, a notoriously difficult board game. Over 200 million people are said to have watched the win.
Throughout the game, the AI made unexpected plays that perplexed its opponent. “There are no rules in the final version of AlphaGo,” claimed Demis Hassabis, co-founder and CEO of DeepMind. “Instead, it learns the game from the ground up by playing thousands of times against different versions of itself, slowly learning through a trial-and-error method known as reinforcement learning.” This implies it can learn the game without being bound by conventional wisdom.” Reward maximization took the place of these limits.
How does a reward system work?
For animals, rewards are a typical learning motivator. In its pursuit of nuts, a squirrel, for example, develops intellectual ability. In the meanwhile, a child may receive chocolate for cleaning their room or a spanking for misbehaving. (Don’t worry, I’m not a parent.)
The rewards and punishments in AI systems are computed analytically. When a self-driving car meets a wall, it gets a -1, and when it safely passes another car, it gets a +1. The agent can assess its performance using these signals. The algorithm then uses trial and error to learn how to maximize the reward and, in the end, complete the task in the most desirable way. Precup’s coworkers are currently working on multi-purpose RL agents.
MuZeru, a program that figures out the laws of a game it’s never seen before, was unveiled by DeepMind in 2020. The lab believes that such agents could eventually tackle a variety of problems in the real world.
There are still significant obstacles to overcome. In complicated situations, RL agents struggle to maximize rewards and understand the long-term consequences of their actions. Nonetheless, proponents of the reward-is-enough approach believe that the algorithms’ adaptability will pave the way toward AGI.
Application of Reinforcement Learning
Here we have examples of reinforcement learning or example of reinforcement learning:
Self-driving vehicle applications
Several papers have advocated Deep Reinforcement Learning for self-driving vehicles. There are several factors to consider in self-driving automobiles, including speed limitations in various locations, drivable zones, and avoiding crashes, to name a few.
Trajectory optimization, motion planning, dynamic pathing, controller optimization, and scenario-based learning policies for highways are some of the autonomous driving activities where reinforcement learning could be used.
Learning automated parking policies, for example, can help with parking. Q-Learning can be used to change lanes, and overtaking can be done by learning an overtaking policy while avoiding collisions and maintaining a constant speed afterward.
The AWS DeepRacer is an autonomous racing automobile that was created to put RL to the test on a real-world track. It controls the throttle and direction using a reinforcement learning model and cameras to visualize the runway.
Reinforcement Learning for Industry Automation
Learning-based robots are utilized to execute many jobs in industry reinforcement. Apart from being more efficient than humans, these robots are also capable of performing activities that would be dangerous for humans.
Deepmind’s usage of AI agents to cool Google Data Centers is a wonderful example. This resulted in a 40% reduction in energy consumption. The AI system currently controls the centers completely without the need for human involvement. Data center experts are evidently still in charge of supervision.
Trading and finance: Uses of reinforcement learning
Forecasting future sales and stock prices can both be done with supervised time series models. These models, on the other hand, do not determine what to do at a given stock price. This is where Reinforcement Learning comes in (RL). An RL agent can select whether to hold, buy, or sell a task. To guarantee that the RL model is working optimally, it is assessed using market benchmark standards.
Unlike prior techniques, which required analysts to make each and every choice, automation ensures uniformity throughout the process. IBM, for example, has developed a sophisticated reinforcement learning-based platform that can execute financial transactions. Every financial transaction’s loss or profit is used to calculate the reward function.
Reinforcement Learning in NLP (Natural Language Processing)
ext summarization, question answering, and machine translation are just a few of the applications of RL in NLP. Eunsol Choi, Daniel Hewlett, and Jakob Uszkoreit, the authors of this study, suggest an RL-based strategy for question answering given large texts. Their method works by selecting a few pertinent sentences from the document to answer the query first. The solutions to the selected sentences are then generated using a slow RNN.
Applications of Reinforcement Learning in Healthcare
Patients in healthcare can benefit from policies learned through RL systems. Without prior knowledge of the mathematical model of biological systems, RL can develop optimal policies based on previous experiences. It makes this method more applicable in healthcare than other control-based systems.
Dynamic treatment regimens (DTRs) in chronic disease or critical care, automated medical diagnostics, and other general fields are examples of RL in healthcare.
Engineering uses of reinforcement learning
In the field of engineering, Facebook has created Horizon, an open-source reinforcement learning platform. Reinforcement learning is used to optimize large-scale production systems on the platform. Horizon has been used by Facebook internally:
- To personalize suggestions
- Deliver more meaningful notifications to users
- Optimize video streaming quality.
Horizon also contains workflows for:
- Simulated environments
- A distributed platform for data preprocessing
- training and exporting models in production.