The REINFORCE model with structured data is a reinforcement learning algorithm that combines the traditional REINFORCE algorithm with the usage of structured data. REINFORCE is a policy-based method that learns to maximize the expected cumulative reward by directly optimizing the policy. By incorporating structured data, this model leverages additional information to make more informed decisions and improve its learning capability.
Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto - This book provides a comprehensive introduction to reinforcement learning, covering both the REINFORCE algorithm and the integration of structured data.
TensorFlow RL: Reinforcement Learning with TensorFlow - This TensorFlow package provides various reinforcement learning algorithms, including implementations that integrate structured data.
OpenAI Gym: A Toolkit for Developing and Comparing Reinforcement Learning Algorithms - OpenAI Gym provides a collection of environments to test and benchmark reinforcement learning algorithms, making it a valuable resource for implementing the REINFORCE model with structured data.
Note: The expertise of these individuals is not solely focused on the REINFORCE model with structured data, but they are highly regarded in the field of reinforcement learning and their GitHub profiles contain relevant resources and implementations in this domain.