Proximal Policy Optimization (PPO) is a reinforcement learning algorithm designed to improve training stability by preventing overly large policy updates. The core idea is to measure the change between the current and previous policy using a ratio, and then clip this ratio within a range — typically [1-ε, 1+ε] — to avoid destructive weight updates.
The Intuition Behind PPO
Empirically, smaller policy updates during training are more likely to converge to an optimal solution. A too-large step can cause the policy to fall "off the cliff," resulting in a bad policy from which recovery may be slow or impossible. Hence, PPO updates the policy conservatively.
The Clipped Surrogate Objective
Traditional policy gradient methods (like REINFORCE) adjust the policy based on the expected reward. However, the step size is critical: too small makes training slow, too large introduces high variability. PPO introduces a clipped surrogate objective function to constrain the policy update within a small range.
The objective is: L^CLIP(θ) = E[min(r(θ)Â, clip(r(θ), 1-ε, 1+ε)Â)]
Where r(θ) = π_θ(a|s) / π_old(a|s) is the probability ratio. The clipping ensures the ratio doesn't deviate far from 1, effectively limiting the policy update.
Visualizing the Clipped Surrogate Objective
When the ratio is within [1-ε, 1+ε], the objective equals the unclipped value. When outside, the gradient is zero, preventing the policy from moving too far. This ensures stable training by balancing exploration and exploitation.
Coding a PPO Agent
After understanding the theory, we can implement PPO from scratch using PyTorch. The algorithm can be applied to environments like CartPole-v1 and LunarLander-v2 to see its effectiveness in practice.
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