Overview of exploitation vs exploration tradeoff in reinforcement learning
Notes that exploration remains an open research problem in RL
References forward dynamics and exploration-via-disagreement methods
Key quotes
“Modern RL algorithms that optimize for the best returns can achieve good exploitation quite efficiently, while exploration remains more like an open topic.”
Transcript
[Updated on 2020-06-17: Add “exploration via disagreement” in the “Forward Dynamics” section . Exploitation versus exploration is a critical topic in Reinforcement Learning. We’d like the RL agent to find the best solution as fast as possible. However, in the meantime, committing to solutions too quickly without enough exploration sounds pretty bad, as it could lead to local minima or total failure. Modern RL algorithms that optimize for the best returns can achieve good exploitation quite efficiently, while exploration remains more like an open topic.
