On the Unreasonable Efficiency of State Space Clustering in Personalization Tasks
This is an incremental improvement for personalization tasks in reinforcement learning.
The paper tackles personalization tasks with complex reward signals using reinforcement learning with state space clustering, demonstrating that this technique accelerates learning without restricting performance.
In this effort we consider a reinforcement learning (RL) technique for solving personalization tasks with complex reward signals. In particular, our approach is based on state space clustering with the use of a simplistic $k$-means algorithm as well as conventional choices of the network architectures and optimization algorithms. Numerical examples demonstrate the efficiency of different RL procedures and are used to illustrate that this technique accelerates the agent's ability to learn and does not restrict the agent's performance.