PPO (Proximal Policy Optimization) is one of the most widely used reinforcement learning algorithms, actively employed in practical RL systems including simulators, trading, robotics, and dialogue agents.
It was developed by OpenAI as an alternative to the more complex and sensitive TRPO (Trust Region Policy Optimization) and became the gold standard thanks to its combination of training stability, simplicity of implementation, and good convergence without complex math.
- training stability
- simplicity of implementation
- good convergence without complex math
Why PPO is needed: the problem with classical policy gradient
In basic policy-gradient algorithms (e.g., REINFORCE), the policy is updated in the direction:
Δθ ∝ ∇_θ log π_θ(a|s) · advantage
Issues with aggressive updates
However, this approach has a serious problem: the update can be too aggressive — the policy changes drastically, and the agent forgets useful behavior or becomes unstable.
- Sharp policy shifts → catastrophic degradation of behavior.
- Without limiting the update magnitude — even a well-trained policy can be ruined.
PPO idea: bounding the update step
PPO solves this very elegantly: it does not allow the policy to deviate too far from the previous one. Instead of a hard mathematical constraint (as in TRPO), PPO uses a "clipped" objective function that softly penalizes overly large updates.
Key idea: update the policy only if the improvement is not too far from the current policy.
How it is done: the clipped objective
Instead of an ordinary gradient, PPO uses the ratio of probabilities of the old and new policies:
r(θ) = π_θ(a|s) / π_θ_old(a|s)
PPO clipped objective
This ratio shows how much the new behavior differs from the old.
PPO objective function (clipped variant):
L(θ) = E[min(r(θ) · Â, clip(r(θ), 1-ε, 1+ε) · Â)]
Components and properties
- Â — advantage estimate.
- ε — allowed deviation (typically 0.1–0.3).
- If r(θ) deviates strongly from 1, the gradient is zeroed out — there is no point in performing a dangerous update.
- This ensures stability while preserving learnability.
- Simple implementation, with no Hessian matrices or KL divergence.
Architecture of a PPO agent
Typically Actor-Critic is used:
- Actor (Policy): produces a distribution over actions.
- Critic (Value Function): estimates the expected return from the current state.
Data flows
- The agent interacts with the environment.
- Trajectories are collected (states, actions, rewards).
- The advantage is computed.
- The actor is updated via the clipped policy loss.
- The critic is updated via MSE between predicted and actual return.
Features and nuances of PPO
- Supports batch processing: you can collect multiple episodes and train on them.
- Works with continuous and discrete actions.
- Resistant to overfitting: thanks to the constraint on the policy step.
- Heuristic entropy regularization: a penalty for overly confident behavior is often added to the objective — this stimulates exploration of new actions.
- Generalized Advantage Estimation (GAE) capability: improves the stability of the advantage estimate.
When and why to choose PPO
When to apply:
- When stability and ease of debugging matter.
- When the environment is expensive (many agents, physical simulation).
- When you need an industrial-grade result with minimal surprises.
Why PPO works well
- Because it limits the risk of destroying the policy.
- And because it uses the maximum available information, but with controlled deviations.
Visual summary of PPO behavior
- Step 1 — Collects data from the environment
- Step 2 — Computes advantage
- Step 3 — Updates the policy, but not too sharply
- Step 4 — Repeats on new data
- Step 5 — Gradually converges to stable behavior
Conclusion
PPO is a powerful and intuitive behavior-learning algorithm that:
- does not require complex tuning,
- provides excellent stability,
- scales and works across diverse tasks.
Precisely thanks to its simple yet effective constraint on the update step, PPO becomes a universal solution in many applied RL tasks.