[Reinforcement Learning] How Proximal Policy Optimization (PPO) updates the policy

PPO Policy Update Mechanism

PPO employs a specialized objective function with a clipping term to manage the policy updates. This function is designed to balance between making the most significant possible improvement in policy performance and avoiding excessively large updates that could lead to performance degradation.

1. Objective Function

The primary objective function in PPO is given by:

$L^{CLIP}(\theta )={\hat{\mathbb{E}}}_t\left[\min (r_t(\theta )A_t,\text{clip}(r_t(\theta ),1-ϵ,1+ϵ)A_t)\right]$

Where:

• r t ( θ