What does "Lifelong Reinforcement Learning" mean?
Table of Contents
Lifelong reinforcement learning is a way for computer programs to learn from different tasks over time. Instead of focusing on just one task, these programs can switch between many tasks, gaining experience that helps them get better in new situations.
How It Works
In lifelong reinforcement learning, a program, or agent, is designed to tackle a series of tasks. Each task adds to the agent's skills and knowledge. This method mimics how humans learn, as we often build on what we have already learned when facing new challenges.
The Role of Policies
A key part of this learning is the concept of a policy. A policy is like a set of rules or a guide that the agent uses to make decisions. In lifelong reinforcement learning, the agent develops a shared policy that helps it adapt quickly to new tasks while still remembering important lessons from previous ones.
Detecting Contexts
To manage different tasks effectively, the agent needs to recognize when it is facing a new task. This is called context detection. By labeling tasks and using special methods to compare old and new information, the agent can decide how to respond based on what it has learned before.
Benefits
The approach allows agents to perform better over time without forgetting what they have already learned. This can lead to more effective learning and better performance in a variety of environments, making it suitable for real-world applications.