Multiepisode Models
- Learns from a series of interactions with an environment rather than from a single episode.
- Enables continual improvement as the model receives feedback and encounters new or changing situations.
- Useful for tasks that require adaptation over time.
Definition
Section titled “Definition”Multiepisode models are a type of reinforcement learning algorithm that can learn from multiple episodes of an environment. This means that the model can learn from a series of interactions with the environment, rather than just a single episode.
Explanation
Section titled “Explanation”By collecting experience across multiple episodes, a multiepisode model updates its predictions or action choices based on feedback received from the environment. Over time this process allows the model to adjust its behavior, adapt to changing circumstances, and improve performance when it encounters new situations.
Examples
Section titled “Examples”Online advertising
Section titled “Online advertising”An online advertising company uses a multiepisode model to optimize its ad targeting strategy. The model receives input about the user’s demographics, browsing history, and other relevant information, and it outputs a prediction of the likelihood that the user will click on an ad. Over time, the model learns from the feedback it receives from the environment (i.e., whether the user actually clicks on the ad or not) and adjusts its predictions accordingly. This allows the model to continually improve its ad targeting strategy and ultimately increase the company’s revenue.
Self-driving cars
Section titled “Self-driving cars”A self-driving car company uses a multiepisode model to teach its vehicles how to navigate roads and avoid obstacles. The model receives input from the car’s sensors, such as cameras and lidar, and it outputs a prediction of the actions the car should take (e.g., accelerate, turn left, or brake). The model learns from the feedback it receives from the environment (i.e., whether the car successfully avoids obstacles and reaches its destination) and adjusts its predictions accordingly. This allows the model to continually improve its decision-making and ultimately make the self-driving car safer and more efficient.
Use cases
Section titled “Use cases”- Optimizing ad targeting strategies in online advertising.
- Teaching self-driving vehicles to navigate roads and avoid obstacles.