On-Policy Prediction with Approximation (chizkidd.github.io)

A recent study has advanced the field of reinforcement learning (RL) by exploring function approximation in the context of estimating the state-value function using on-policy data. This research focuses on how to effectively approximate the value function \(v_\pi\) through parameterized forms, including linear functions, neural networks, and decision trees. This is significant for the AI/ML community as it addresses a crucial challenge in RL: applying function approximation to partially observable environments where full state information is not accessible to the agent. The study introduces a framework where updates to the value function are guided by a mean squared value error metric, weighted by the agent's state distribution. This allows practitioners to utilize various established supervised learning techniques for real-time value estimation while maintaining the capability for online learning. The research further discusses the convergence properties of Stochastic Gradient Descent methods and highlights the importance of properly selecting state representations and features, as these choices can notably impact the learning process and performance of RL systems. Overall, this work lays a foundation for improving the efficiency and effectiveness of RL strategies in complex, dynamic environments.