Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning (REINFORCE)

Derived the Policy Gradient Theorem and the REINFORCE algorithm, enabling gradient ascent directly on parameterized policy distributions via log-derivative likelihood tricks.

Serves as qualified theoretical and systems foundation for task-reinforcement-learning.

Assumptions

• Markovian state dynamics and stationary reward functions hold in target evaluation environments

Limitations

• Sample efficiency, exploration stability, and real-world sim-to-real transfer gaps require specialized tuning