10.3 Proximal Policy Optimization (PPO)

What if you could move fast — but with guardrails that keep you from tipping over? 🎛️

TRPO’s main drawback has to do with the calculation of the Hessian matrix with respect to the KL-Divergence:

\[ \mathbf{H} = \nabla^2 D_{KL}(\pi_{\theta_{t}} \| \pi_{\theta_{t+1}}) \]

WarningProblem

How can we design an algorithm that achieves stable policy updates like TRPO, but avoids the computational complexity of calculating the Hessian matrix? Could clipping the probability ratio be a simpler yet effective solution?