Proximal Policy Optimization (PPO)

> A2C throws away each rollout after one update. PPO wraps the policy gradient in a clipped importance ratio so you can do 10+ epochs on the same data without the policy exploding. Schulman et al. (2017). Still the default policy-gradient algorithm in 2026.

Type: Build

Languages: Python

Prerequisites: Phase 9 · 06 (REINFORCE), Phase 9 · 07 (Actor-Critic)

Time: ~75 minutes

The Problem

A2C (Lesson 07) is on-policy: the gradient E_{π_θ}[A · ∇ log π_θ] requires data sampled from the *current* π_θ. Take one update, and π_θ changes; the data you used is now off-policy. Re-use it and your gradient is biased.

Rollouts are expensive. On Atari, one rollout across 8 envs × 128 steps = 1024 transitions and a dozen seconds of environment time. Throwing that away after one gradient step is wasteful.

Trust Region Policy Optimization (TRPO, Schulman 2015) was the first fix: constrain each update so the KL divergence between old and new policy stays below δ. Theoretically clean, but requires a conjugate-gradient solve per update. Nobody runs TRPO in 2026.

PPO (Schulman et al. 2017) replaces the hard trust-region constraint with a simple clipped objective. One extra line of code. Ten epochs per rollout. No conjugate gradients. Good-enough theoretical guarantees. Nine years later it is still the default policy-gradient algorithm for everything from MuJoCo to RLHF.

The Concept

The importance ratio.

r_t(θ) = π_θ(a_t | s_t) / π_{θ_old}(a_t | s_t)

This is the likelihood ratio of the new policy vs the policy that collected the data. r_t = 1 means no change. r_t = 2 means the new policy is twice as likely to take a_t as the old.

The clipped surrogate.

L^{CLIP}(θ) = E_t [ min( r_t(θ) A_t, clip(r_t(θ), 1-ε, 1+ε) A_t ) ]

Two terms:

• If the advantage A_t > 0 and the ratio tries to grow past 1 + ε, the clip flattens the gradient — don't push a good action further than +ε above old probability.
• If the advantage A_t < 0 and the ratio tries to grow past 1 - ε (meaning we would make a bad action more likely compared to its clipped reduction), the clip caps the gradient — don't push a bad action below -ε.

The min handles the other direction: if the ratio has moved in the *beneficial* direction, you still get the gradient (no clipping on the side that would hurt you).

Typical ε = 0.2. Plot the objective as a function of r_t: a piecewise-linear function with a flat roof on the "good side" and a flat floor on the "bad side."

The full PPO loss.

L(θ, φ) = L^{CLIP}(θ) - c_v · (V_φ(s_t) - V_t^{target})² + c_e · H(π_θ(·|s_t))

Same actor-critic structure as A2C. Three coefficients, usually c_v = 0.5, c_e = 0.01, ε = 0.2.

The training loop.

1. Collect N × T transitions across N parallel envs for T steps each.
2. Compute advantages (GAE), freeze them as constants.
3. Freeze π_{θ_old} as a snapshot of current π_θ.
4. For K epochs, for each minibatch of (s, a, A, V_target, log π_old(a|s)):

- Compute r_t(θ) = exp(log π_θ(a|s) - log π_old(a|s)).

- Apply L^{CLIP} + value loss + entropy.

- Gradient step.

1. Discard the rollout. Return to step 1.

K = 10 and minibatches of 64 is a standard hyperparameter set. PPO is robust: the exact numbers rarely matter within ±50%.

KL-penalty variant. The original paper proposed an alternative using an adaptive KL penalty: L = L^{PG} - β · KL(π_θ || π_old) with β adjusted based on observed KL. The clipping version became dominant; the KL variant survives in RLHF (where KL to the reference policy is a separate constraint you always want anyway).

Build It

Step 1: capture log π_old(a | s) at rollout time

for step in range(T):
    probs = softmax(logits(theta, state_features(s)))
    a = sample(probs, rng)
    s_next, r, done = env.step(s, a)
    buffer.append({
        "s": s, "a": a, "r": r, "done": done,
        "v_old": value(w, state_features(s)),
        "log_pi_old": log(probs[a] + 1e-12),
    })
    s = s_next

The snapshot is taken once, at rollout time. It does not change during the update epochs.

Step 2: compute GAE advantages (Lesson 07)

Same as A2C. Normalize across the batch.

Step 3: clipped surrogate update

for _ in range(K_EPOCHS):
    for mb in minibatches(buffer, size=64):
        for rec in mb:
            x = state_features(rec["s"])
            probs = softmax(logits(theta, x))
            logp = log(probs[rec["a"]] + 1e-12)
            ratio = exp(logp - rec["log_pi_old"])
            adv = rec["advantage"]
            surrogate = min(
                ratio * adv,
                clamp(ratio, 1 - EPS, 1 + EPS) * adv,
            )
            # backprop -surrogate, add value loss, subtract entropy
            grad_logpi = onehot(rec["a"]) - probs
            if (adv > 0 and ratio >= 1 + EPS) or (adv < 0 and ratio <= 1 - EPS):
                pg_grad = 0.0  # clipped
            else:
                pg_grad = ratio * adv
            for i in range(N_ACTIONS):
                for j in range(N_FEAT):
                    theta[i][j] += LR * pg_grad * grad_logpi[i] * x[j]

The "clipped → zero gradient" pattern is the heart of PPO. If the new policy has already drifted too far in the beneficial direction, the update stops.

Step 4: value and entropy

Add standard MSE to the critic target and an entropy bonus on the actor, same as A2C.

Step 5: diagnostics

Three things to watch every update:

• Mean KL E[log π_old - log π_θ]. Should stay in [0, 0.02]. If it blows past 0.1, reduce K_EPOCHS or LR.
• Clip fraction — the fraction of samples whose ratio lies outside [1-ε, 1+ε]. Should be ~0.1-0.3. If ~0, the clip never triggers → raise LR or K_EPOCHS. If ~0.5+, you are over-fitting the rollout → lower them.
• Explained variance 1 - Var(V_target - V_pred) / Var(V_target). Critic quality metric. Should climb toward 1 as the critic learns.

Pitfalls

• Clip coefficient mistuned. ε = 0.2 is the de-facto standard. Going to 0.1 makes updates too timid; 0.3+ invites instability.
• Too many epochs. K > 20 routinely destabilizes because the policy drifts far from π_old. Cap epochs, especially for large networks.
• No reward normalization. Large reward scales eat into the clip range. Normalize rewards (running std) before computing advantages.
• Forgetting advantage normalization. Per-batch zero-mean/unit-std normalization is standard. Skipping it wrecks PPO on most benchmarks.
• Learning rate not decayed. PPO benefits from linear LR decay to zero. Constant LR is often worse.
• Importance ratio math errors. Always exp(log_new - log_old) for numerical stability, not new / old.
• Wrong gradient sign. Maximize the surrogate = *minimize* -L^{CLIP}. A flipped sign is the most common PPO bug.

Use It

PPO is 2026's default RL algorithm across a surprising number of domains:

The PPO *loss shape* — clipped surrogate + value + entropy — is the scaffolding for DPO, GRPO, and nearly every RLHF pipeline.

Ship It

Save as outputs/skill-ppo-trainer.md:

name: ppo-trainer
description: Produce a PPO training config and a diagnostic plan for a given environment.
version: 1.0.0
phase: 9
lesson: 8
tags: [rl, ppo, policy-gradient]
---

Given an environment and training budget, output:

1. Rollout size. `N` envs × `T` steps.
2. Update schedule. `K` epochs, minibatch size, LR schedule.
3. Surrogate params. `ε` (clip), `c_v`, `c_e`, advantage normalization on.
4. Advantage. GAE(`λ`) with explicit `γ` and `λ`.
5. Diagnostics plan. KL, clip fraction, explained variance thresholds with alerts.

Refuse `K > 30` or `ε > 0.3` (unsafe trust region). Refuse any PPO run without advantage normalization or KL/clip monitoring. Flag clip fraction sustained above 0.4 as drift.

Exercises

1. Easy. Run PPO on 4×4 GridWorld with ε=0.2, K=4. Compare sample efficiency to A2C (one epoch per rollout) at matched env steps.
2. Medium. Sweep K ∈ {1, 4, 10, 30}. Plot return vs env steps and track mean KL per update. At what K does KL explode on this task?
3. Hard. Replace the clipped surrogate with an adaptive KL penalty (β doubled if KL > 2·target, halved if KL < target/2). Compare final return, stability, and clip-free-ness.

Key Terms

Further Reading

• Schulman et al. (2017). Proximal Policy Optimization Algorithms — the paper.
• Schulman et al. (2015). Trust Region Policy Optimization — TRPO, PPO's predecessor.
• Andrychowicz et al. (2021). What Matters In On-Policy RL? A Large-Scale Empirical Study — every PPO hyperparameter ablated.
• Ouyang et al. (2022). Training language models to follow instructions with human feedback — InstructGPT; the PPO-in-RLHF recipe.
• OpenAI Spinning Up — PPO — clean modern exposition with PyTorch.
• CleanRL PPO implementation — reference single-file PPO used by many papers.
• Hugging Face TRL — PPOTrainer — the production recipe for PPO on language models; read alongside Lesson 09 (RLHF).
• Engstrom et al. (2020). Implementation Matters in Deep Policy Gradients — the "37 code-level optimizations" paper; which PPO tricks are load-bearing and which are folklore.