Trust Region Policy Optimization (TRPO)

Guaranteed monotonic policy improvement by enforcing a Kullback-Leibler (KL) divergence trust-region constraint between old and new policies solved with conjugate gradient and Fisher information.

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