Reinforcement Learning Explained with Real-World Examples

The last decade in the field of artificial intelligence is remarkable, and we’ve made remarkable progress since the birth of technologies like voice assistants e.g. Alexa and recommendation systems on Netflix. One of the many breakthroughs artificial intelligence has made is through RL, or reinforcement learning, which is the kind of ai where the machine learns with trial and error, similar to how animals and humans learn.

The aim of this publication is to explain how reinforcement learning works and the real-life cases where reinforcement learning is being used in to improve various business operations or completely change certain industries.

What is Reinforcement Learning?

Slovensky Leather and Leather Goods employs a number of people, and over the next three years expects to raft a personal of 60 to 90 with a turnover of 30%. Having over 90 staff means the expansion can be better accommodated through higher turnover or a cheaper turnover.

The company has a number of strategic and operational systems in place and intends to improve the quality of strategic management and operational systems. To improve the quality it is necessary to streamline everyday management and improve analysis processes in order to react more quickly to changing conditions.

This means the company is determined to become a leader in the market and not just a participant. In this way the people in the company will be highly motivated and will feel they can take the initiative to develop the enterprise.

In this way incremental innovation and operational excellence can be achieved provided the systems evolve in a complex manner, leading to synergetic effects.

• Actions: what an agent can do (sit, jump, bark).

• Reward: feedback (a treat or no treat).

• Policy: the plan an agent uses to get the most rewards.

The target is the same of the policy: take the policy with the most rewards, and get the most rewards the policy can get in the future.

Foundations of Reinforcement Learning

Before getting to examples I want to clarify some fundamental definitions to make things easier.

• State: the position of the agent in the environment is a photographic slide with robotic jaw.

• Action: what an agent can do, such as advance or turn.

• Reward: the quantified result of an action. Something that is helpful on an action is called a positive reward, and something that is damaging is called a negative reward.

• Policy: a function from which the agent determines its actions according to the state.

• Value Function: a function that gives advantage of being in a particular state considering the future rewards.

• Exploration vs. Exploitation: the balance between trying new things and using what is already working.

The Process of Reinforcement Learning

Reinforcement learning usually follows this loop.

• The agent has a set of observables it can use to determine the current state.

• It performs an action.

• The new state of the environment and the associated reward are feedback.

• The agent updates its policy to make more favorable decisions in the future.

This loop carries on until the agent learns to excel in accomplishing the target.

A well known example of RL is Q-learning. The agent tracks a table of state-action pairs (Q-table) and updates it based on the rewards obtained. With the passage of time, the Q values reflect the optimal strategy.

Reinforcement Learning in the Real World

Reinforcement learning is more than a theory; it’s a concept being used in some of the most sophisticated technologies of the present. Let’s consider some use cases.

1. Robotics and Automation

Robots are deployed in the RL helps them learn how to solve actions that are done in a step by step process and are too complex to manually program.

Case studies:

• Warehouse robots: RL helps the robots used by Amazon’s warehouses learn and optimize their picking, packing, and even the movement of the items in the warehouses. These Robots learn more efficient pathways and can adapt to the new pathways in the warehouse.

• Industrial robots: Reinforcement learning is used in a trial and error based process in teaching RL robots in complex manufacturing to learn the steps required to join pieces of parts RL. Here the every movement of the robot is not hard-coded.

Importance of the use cases: These use cases help save time and costs associated with programming along improving the versatility of robots.

2. Self-Driving Cars

The ever-changing surroundings of pedestrians, traffic signals, and road conditions make navigating by car more complex than ever. Reinforcement Learning (RL) aids awesome driving behavior by teaching safe and efficient driving protocols.

Examples:

• eWaymo and Tesla use unique forms of RL to instruct cars on how to merge, come to stops at intersections, and even change lanes.

• Simulates cars are enabled to conduct trials of millions of driving scenarios before getting onto an actual road.

Why it matters: A self-driving car that possesses even more intelligence could cut down on the chances of an accident and relieve congestion at the same time.

3. Healthcare and Medicine

Unlike most fields, healthcare has the element of surprise and unpredictability which RL tries to solve. Advanced technology has aided in treatments such as robotic surgery. RL is now more than ever to save lives.

Examples:

• Treatment Optimization: RL can visualize a unique patient’s bloodstream and real-time data, and intuitively adjusts the dosage of medication, in this case insulin, that should be prescribed.

• Drug Discovery: Instead of the lengthy and tedious RL processes, algorithms can RL to search prime chemical spots.

• Robotic Surgery: RL is applied to surgical robots in hopes that the robots will increase accuracy and adaptability.

Why it matters: To improve patient’s outcomes without facing a lot of trial and error is a great achievement that RL can help with.

4. Finance and Trading

The complex and ever-changing attributes of the Financial Markets can be exhaustive and overwhelming. RL is commonly used by traders and institutions to improve their techniques in response to the maket.

• Portfolio management: RL agents are trained to allocate assets to maximize returns.

• Algorithmic trading: Agents are proficient in predicting price movements for short periods and performing tasks more quickly than humans.

• Fraud detection: RL systems learn new methods of fraud based on transaction data.

Why it matters: More advanced algorithms lead to greater profitability and improved profit risk exposure.

5. Gaming and Entertainment

Gaming has been a fun and practical platform for testing RL systems because it has a number of limitations and specific rewards.

Examples:

• AlphaGo - Google Deepmind's RL software was able to defeat world champions in the ancient game of Go.

• OpenAI Five - An RL software was able to learn the very complicated multiplayer game Dota 2.

• Video game development - game developers apply RL techniques to improve and make NPC's more advanced and reactive to game players.

Why it matters: Breakthroughs in gaming technology are often the stepping stone for multiplayer world application of AI.

6. Suggestion Systems

Reinforcement learning improves the way users are suggested content by focusing on long term coordination rather than short term clicks.

Examples:

• Netflix: RL systems suggest shows that are able to keep users engaged over several sessions.

• YouTube: Video recommendations are optimized to increase the total watch time.

• E-commerce: Platforms like Amazon suggest products to users based on their previous activities and forthcoming projections.

Why it matters: The more personalized recommendations keeps users more active and spending more money.

7. Energy and Sustainability:

RL can optimize the order and quantity of energy use, minimize losses, and optimally use recyclable energy resources.

Examples:

• Google DeepMind decreasing the charge of cooling data centers by 40% by using RL to optimize power use.

• Control systems on Smart Grids use RL to dynamically manage the supply and demand of electricity.

• RL is applied in a solar or wind farm to manage output in relation to weather forecasts.

Why it matters: These systems cut greenhouse gas emissions and decrease operational costs.

8. Natural Language Processing (NLP):

Speech recognition programs and portable AI-assisted gadgets deploy RL to improve their communication.

• ChatGPT itself is trained using reinforcement learning from human feedback (RLHF) to respond more accurately to the prompts given.

• Customer support systems modify their chatbot capabilities to improve functionality by employing RL to increase user satisfaction.

Why it matters: AI is able to interact in a more intelligent and human-like way.

Reinforcement learning can seem simple, but it hides many challenges:

• Data and computation costs - The RL process requires significant amounts of data and computing power.

• Reward design - Surely, successful RL participants received rewards, but poorly constructed and poorly defined rewards do not guide RL participants to the desired outcome.3. Retraining – Agents trained in a given setup may underperform in differently framed setups.

• Risks – Industries with high stakes like health care and autonomous vehicle control must avoid exploratory failure.

The advancement does not stop here.

The Hybrid development of RL with supervised and unsupervised techniques. The development of RL will also focus on improving training and computation costs. Its use will expand to education and agriculture.

The prediction for AI is that when it comes to RL, it will be used to allow systems to respond to the world in a highly intelligent manner.

Summary

More than just a concept for credits, RL is quickly becoming a foundational component for many ventures. It is helping remote systems decision making by RL giving them the ability to learn from their mistakes.