Episodic vs. Continuous Reinforcement Learning: A Game-Changing Perspective
When it comes to training intelligent agents, Reinforcement Learning (RL) is a fundamental concept. A common misconception is that RL is a one-size-fits-all approach. However, there are two distinct subfields: Episodic and Continuous RL. To illustrate this, let's consider an agent navigating a game environment.
Episodic RL: The Level-Based Approach
Imagine an agent playing a game divided into discrete levels or episodes. Each episode represents a complete level, and the agent's state resets after completing it. In Episodic RL, the agent learns by interacting with the environment within each episode, receiving rewards or penalties accordingly. Think of it like completing a level in a video game: you receive a reward for completing the level, and your state resets for the next level. This approach is suitable for tasks with clear objectives, such as completing a level or achieving a specific goal.
...
This post was originally shared as an AI/ML insight. Follow me for more expert content on artificial intelligence and machine learning.
Top comments (0)