Searching for what is reinforcement learning in machine learning? Autonomous systems in machine learning are transforming the way we look at the world in the most unimaginable ways. From self-driving cars to robots, the world around us is becoming autonomous, and one of the key factors in the autonomous industrial revolution is reinforcement learning.
The auto-pilot feature in Tesla cars and Netflix’s show recommendation algorithm uses reinforcement learning alongside other machine learning algorithms. Because of the self-driving feature, Tesla's revenue in June 2023 was $24.927B, which is a 47.2% increase yearlong.
Let’s cover the following topics under this article:
- What is Reinforcement Learning in Machine Learning?
- Types of Reinforcement Learning in Machine Learning
- Key Notions of Reinforcement Learning
- Importance of reinforcement learning in autonomous industry
- Role of Reinforcement Learning in Autonomous Agents: Real-Life Examples
- Challenges of Making Autonomous Systems with Reinforcement Learning
- Upscale Your Knowledge about Reinforcement Learning
- FAQs on Reinforcement Learning
What is Reinforcement Learning in Machine Learning?
Reinforcement learning deals with the odds of decision-making by training an intelligent machine to maximize the rewards by learning to interpret the state of behavior and environment. In reinforcement machine learning algorithms, the learning entity that is being trained gets rewarded for certain “acceptable” behaviors and gets punished for “undesired” ways of behaving. Philosophically, you can say that learning from mistakes is reinforcement learning.
To simplify the concept of what is reinforcement learning, Let’s understand how the trained agent or learning entity acquires “good” behaviors with an example:
- You are in a “state” > You have to move to the “next state” > You take “action” to move to the next state > You get a “reward” for your action > On the basis of the reward, you choose what action to “take next” > This defines the “policy” > The policy helps you in taking “optimal actions” > This eventually results in “maximum rewards.”
Types of Reinforcement Learning in Machine Learning
Everyone has a different definition when it comes to distinguishing the types of reinforcement learning. Still, it can be categorized into which reinforcement models address complicated situations and environments:
Value-Based Reinforcement Learning:
This learning takes a value-based function and optimizes it to the point where intelligent learners or agents try to reach from a certain state to the policy state in order to gain rewards. The value-based function predicts the maximum expected rewards an agent will gain on an individual state.
Model-Based Reinforcement Learning:
As the name suggests, model-based learning creates a model of the environment for the agent to train. This learning model represents and forms a behavioral environment for the learning entity to form strategies for making future decisions.
Policy-Based Reinforcement Learning:
In this learning model, the agent directly aims to learn from the best policy to find optimal behavior instead of using a value-based function. The policy-gradient function is optimized and plotted from state to behavior instead of approximating value functions. The agent’s optimal behavior is determined by a policy-based function aiming to maximize the rewards.
- Some of the Principles Used Across different Categories of Reinforcement Learning:
- Deep Q-Networks (DQN)
- Dynamic Programming
- Monte Carlo Tree Search (MCTS)
Key Notions of Reinforcement Learning
Now that you know the basics of what is reinforcement learning in machine learning, you might be wondering about some of the concepts behind reinforcement learning to make systems autonomous. The most basic concept behind it is that an agent associates with a state of the environment and adapts its behavior in order to make accurate decisions by receiving constructive criticism in the form of rewards or punishments.
Importance of Reinforcement Learning in Autonomous Industry
Since the twentieth century, there has been a challenge to automate manual labor and reduce the burden on human shoulders. Everything, from machinery to finances, was handled by humans. Radio-controlled cars in the early twentieth century and the DARPA challenge in the early 2000s were attempts to accelerate and empower automation. Still, Reinforcement learning was what really boosted and brought it to life.
Some advantages of Reinforcement learning in the Autonomous Sector:
- RL models allow systems and agents to adapt and learn from the experiences and systems kept on improving, even without human intervention with RL.
- It eliminated the need for regular testing in a real-life environment as the simulations helped agents to improve in virtual space.
- Randomness used to be a problem for automated systems, but as policies improved, so did decision-making based on probabilities. Reinforcement learning made it easier to personalize for different use cases and use the same agent for various tasks in a familiar domain.
- With a little human intervention to tune the agents (RLHF), new algorithms were also made to mimic human-like conversation or mimic humans performing tasks.
What do experts say?
“Reinforcement learning is the idea of being able to assign credit or blame to all the actions you took along the way while you were getting that reward signal.”
- Jeff Dean, Chief Scientist at Google AI
“Reinforcement learning is learning from rewards, by trial and error, during normal interaction with the world. This makes it very much like natural learning processes and unlike supervised learning, in which learning only happens during a special training phase in which a supervisory or teaching signal is available that will not be available during normal use.”
- Richard Sutton (Godfather of Reinforcement Learning), Scientist at the University of Alberta
Role of Reinforcement Learning in Autonomous Agents: Real-Life Examples
In the era of abundant data and rapid scientific progress, from Bellman equations to the PPO algorithm introduced by OpenAI, RL took a major leap forward, and the practical implementation is supercharging the process of automation in the industry. Let’s look at the examples of how the major companies use reinforcement learning in the autonomous industry:
Self-driving cars are no longer something out of a science fiction film. Wayve showcased an exceptional demo in 2015, where the agent in the car learns to steer the wheels and learns from wrong decisions. Tesla uses deep reinforcement learning alongside various deep learning methods to develop a more robust and active self-driving model for its car, resulting in more accurate learning and judgments. AWS Deep Racer and Deep Racer EVO allow developers to build better reinforcement learning policies and algorithms by allowing freedom to experiment. There are challenges ahead to scale the models, but with continuous development and improvements, RL is taking center stage in the self-driving domain.
After the 2020 pandemic, the need for transformation in the healthcare industry is greater than ever. Reinforcement learning is being used to assist healthcare decision-making in situations with limited data. In 2021, Microsoft introduced a model called Dead-end Discovery (DeD) to identify high-risk treatments, which reduces the chance of critical errors leading to patient fatalities. This RL agent has the potential to save the lives of patients who are severely ill. Autonomous surgical robots powered by self-learning agents and trained for thousands of hours to execute surgeries requiring pinpoint precision are implemented in hospitals to assist and limit the possibility of errors.
Large Language Models
Imagine an AI that can talk, answer your doubts, give you recipes for your breakfast, and help you with your homework. Large language models like ChatGPT aim to do that and deliver a human-like conversation with state-of-the-art results in chat completion. During ChatGPT training, OpenAI used Reinforcement Learning from Human Feedback (RLHF) and created a new RL policy called Proximal Policy Optimisation (PPO) to train the agents to make better decisions and generate better texts. They made the agent answer four questions, then choose the best response and rewarded the model for producing better results with each cycle.
Human survival depends heavily on the supply of food. Our farmers work tirelessly day and night to harvest these crops, but traditional methods may no longer be the most efficient. From simple reflex agents to model-based reflex agents, multiple automated bots or drones are being developed for seed planting and calculating the effects of various pesticides and insecticides on crops. The main goal of these machine and reinforcement learning-based agents is to power unmanned machines, which reduces the manual work for laborers, resulting in efficient farming methods.
Investments worth billions of dollars happen every day. Investors and traders constantly look out for risk-management tools and reinforcement learning solutions for them. RL Agents can be trained to reduce the burden of customer banking, make better decision-making while trading, make data-driven decisions and help companies gain a competitive advantage. They can also perform statistical modeling pattern recognition and gain deeper insights from data. Agents can be taught to work with little to no human intervention.
Challenges of Making Autonomous Systems with Reinforcement Learning
Although the use of reinforcement machine learning algorithms plays a big part in revolutionizing the autonomous industry, it comes with its set of technical challenges that can be faced during the automation of systems. Let’s encounter some of the technical challenges with autonomous agents using RL:
- Data Scalability: To make the systems autonomous, like self-driving cars and robots, a large amount of data is required in bulk. So that the agents can learn the policies better to avoid any errors in real-world implementation, but it can be time-consuming to collect bulk data for training the agents.
- Unexpected Environments: Sometimes, the environment assigned to the agents can be complicated with complex instructions. It may lead to undesired results from learning agents that might not be useful for logical exploration in complex environments.
- Accuracy with Interactions: Safety is always the first priority, and interaction between AI and humans should be seamless, especially with autonomous vehicles in times of crucial decision-making.
Upscale Your Knowledge about Reinforcement Learning
Exploring AI and machine learning is exciting for both beginners and professionals in the tech industry. Understanding what is reinforcement learning in machine learning requires that your basic concepts of machine learning are crystal clear. The world around us is becoming autonomous, and many aspiring learners want to be part of this change. At Interview Kickstart, join the free webinar to learn more about machine learning and start your journey.
FAQs about Reinforcement Learning
Q1. What makes reinforcement learning unique?
Reinforcement learning does not require training data or labeled data in order to learn as most traditional AI methods do. It acquires training data in terms of actions the model takes and the reward it gets after the action.
Q2. What are the key elements of reinforcement learning?
The 'agent' is the environment in which agents interact; the 'state' is a representation of the environment at the current time, the ‘action’ to take, the ‘'reward' after action, and the ‘policy’ which defines the behavior of the agent are some of the key elements of reinforcement learning.
Q3. Is reinforcement learning a neural network?
Neural networks are not inherently part of reinforcement learning, but combining both of them gave birth to a new domain called deep reinforcement learning.
Q4. What are the best techniques for reinforcement learning?
There are many algorithms available to create models, but prior knowledge of machine learning helps us choose the best one for our use cases. Given that Q-learning, DQN, and PPO are widely used RL techniques.
Q5.Which of the major AI problems can be overcome with deep reinforcement learning?
Although problems are mostly domain-specific, decision-making with randomness in data, adaptability for various tasks, and continuously improving with time are problems that are better solved with RL.