Exploitation Is All You Need... for Exploration

Summary

This paper talks about how meta-reinforcement learning agents can learn to explore their environments effectively even when trained to always choose the best action, as long as certain conditions are met.

What's the problem?

The problem is that teaching AI to explore new possibilities usually requires designing special exploration strategies, but this can be complicated and inefficient.

What's the solution?

The paper shows that if the environment has repeating patterns, the agent can remember past experiences, and it can understand the impact of its actions over a long time, then simply training the agent to be greedy (always picking the best immediate reward) naturally leads to good exploration.

Why it matters?

This matters because it simplifies how AI agents learn to explore, which is important for solving complex tasks where discovering new knowledge or strategies is key to success.