Active vs Passive Reinforcement Learning
In reinforcement learning, an agent learns by interacting with an environment to achieve a goal — like a robot learning to walk or a game character learning to win.
There are two main ways this learning can happen:
- Passive Reinforcement Learning
- Active Reinforcement Learning
Let’s break them down.
What is Passive Reinforcement Learning?
Passive RL means the agent:
- Follows a fixed set of rules (a policy)
- Doesn’t try new actions on its own
- Only learns how good the policy is
It’s like watching a friend play a video game using the same moves every time, and you just observe whether they win or lose. You don’t change the moves — you just figure out which states are good or bad based on the outcome.
Example:
Imagine a delivery robot that always follows the same path from the office to the mailbox. It doesn’t explore new paths. Over time, it learns which areas are usually blocked or slow, but it never tries a new route — just learns how good the current path is.
What is Active Reinforcement Learning?
Active RL means the agent:
- Learns by exploring different actions
- Tries out new things to improve
- Finds the best policy on its own
It’s like playing a game yourself, where you try different strategies and buttons to win more. You actively learn what works best, not just what someone else did.
Example:
A self-driving car in a new city learns to drive efficiently. It tries different routes, avoids traffic, learns shortcuts, and improves over time by exploring and learning from feedback.
Quick Recap
In Passive Reinforcement Learning, the agent follows a fixed policy and just learns how good or bad the outcomes are. In Active Reinforcement Learning, the agent makes its own decisions, explores different actions, and learns the best way to achieve its goal.