Model-based reinforcement learning is a means for machines to make decisions using a predictive model to determine what will happen if a particular course of action is taken to choose the best solution. Let’s break the definition down to understand the concept better.
A predictive model is used in statistics to predict outcomes by analyzing patterns in a data set. Think of it as a list of all possible outcomes from which a machine will choose the one that best suits the problem presented to it.
- How Do Model-Free and Model-Based Reinforcement Learning Differ?
- What Are the Elements of Model-Based Reinforcement Learning?
- What Are the Different Uses of Model-Based Reinforcement Learning?
- Key Takeaways
Read More about Model-Based Reinforcement Learning
Machines generally use two models to learn—model-free and model-based reinforcement learning. How do they differ, though?
How Do Model-Free and Model-Based Reinforcement Learning Differ?
Based on its name, model-free reinforcement learning doesn’t use a predictive model. The machine depends on sampling and simulation to estimate rewards to predict outcomes. It doesn’t need to know the system’s inner workings or how to get to the answer.
What Are the Elements of Model-Based Reinforcement Learning?
Any reinforcement learning requires the same elements, namely:
- Agent: This refers to the program that controls the object of concern. An example would be a robot.
- Environment: This refers to the outside world that has been programmed to serve as the machine’s environment. It includes everything the agent interacts with and is built to put the agent in the real world. The machine needs an environment to show how it will do in the real world.
- Reward: This represents a score showing an algorithm’s performance in its environment. It is represented as “1” or “0,” where 1 means the machine made the right move and 0 means it made the wrong move. A reward, therefore, represents either a gain or a loss.
- Policy: This refers to the algorithm the agent uses to decide how it will act. It’s the element that can be model-based or model-free.
What Are the Different Uses of Model-Based Reinforcement Learning?
Model-based reinforcement learning has various real-world applications, including:
- Autonomous vehicles: Reinforcement learning models train self-driving cars to navigate in a dynamic environment by learning from experiences in minimizing traffic disruption. They learn about driving zones, traffic handling, maintaining the speed limit, and avoiding collisions.
- Data center cooling: Reinforcement learning models use tons of sensors with data centers to collect data, including temperature, power consumption, and others to ensure the servers maintain their optimal temperature to work correctly. The trained machines spot irregularities and address these to cool data centers.
- Traffic light control: Reinforcement learning trains models to control traffic lights based on the current traffic status optimally. It makes decisions based on how congested streets are.
- Healthcare: Reinforcement learning teaches machines to find the right treatments and map them to the right person.
- Automated medical diagnosis: Reinforcement learning trains machines to generate medical reports, identify nodules or tumors and blood vessel blockages, and analyze reports, among others.
- Dynamic treatment: Reinforcement learning enables machines to make healthcare decisions, including treatment type, drug dosages, and appointment timing, tailor-fit to individual patients based on their medical history and conditions over time.
- Robotic surgery: Reinforcement learning enhances the decision-making skills of surgical bots to minimize errors, increasing surgeons’ efficiency. An example is Da Vinci, which lets surgeons perform complex procedures with greater flexibility and control than conventional approaches.
- Image processing: Reinforcement learning allows robots with visual sensors to learn about their surroundings. Examples include closed-circuit television (CCTV) cameras that perform traffic and crowd analytics.
- Robotics: Reinforcement learning makes robots robust and helps them acquire complex behaviors to adapt to different scenarios. Instead of performing time-consuming and tedious checks, warehouses can teach robots to learn independently.
- Natural language processing (NLP): Reinforcement learning trains machines to understand a few sentences in a document and use these to answer corresponding questions.
- Marketing: Reinforcement learning enables customizing customer recommendations based on shopping histories. The sellers’ machines can be trained to spot which ads work from those that don’t.
- Gaming: Reinforcement learning trains a machine to learn on its own by performing actions in the game environment to achieve the desired behaviors.
While many organizations have begun using model-based reinforcement learning in various aspects of their operations, we’re bound to see more real-world applications in the future.
- Model-based reinforcement learning is a technique that uses a predictive model to teach machines to make the best decisions.
- Reinforcement learning—both model-free and model-based—has four elements, namely, agent, environment, reward, and policy.
- Model-based reinforcement learning has several real-world applications, including autonomous vehicles, data center cooling, traffic light control, healthcare, and gaming.