PPO-penalty

As explained in Optimization Algorithms: AI techniques for design, planning, and control problems

PPO-penalty is a proximal policy optimization variant that penalizes divergence between updated and old policies in its objective.

PPO-penalty is a proximal policy optimization variant that limits how far an updated policy moves from the policy that collected the data. This protects learning from unstable updates that make advantage estimates inaccurate and can severely disrupt the policy. section 12.1.5

Its update follows a trust-region idea:

• Adds a penalty for divergence between the updated policy and the old policy to the objective. section 12.1.5
• Measures divergence with Kullback–Leibler (KL) divergence, which quantifies dissimilarity between probability distributions. section 12.1.5
• Uses the penalty coefficient β and optimizes the resulting unconstrained objective with stochastic gradient ascent. section 12.1.5

KL divergence between two Gaussian distributions

Compare KL divergence between two Gaussian distributions to build intuition for how policy dissimilarity is measured. section 12.1.5

How Optimization Algorithms: AI techniques for design, planning, and control problems covers it

How deep it goes

Explains the update objective, penalty coefficient, KL measure, and stochastic optimization, but not implementation details.

Examples

Uses Gaussian-distribution comparisons to visualize KL divergence between policies.

What it assumes

Assumes policy-gradient learning, advantage estimates, probability distributions, and KL divergence.

What you can do after

Evaluate how a policy-update penalty constrains divergence from the data-collecting policy.

How it connects

Connects policy gradients, advantage functions, trust-region policy optimization, PPO-clip, and KL divergence.

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Related concepts

• Proximal Policy Optimizationkind of · Proximal Policy Optimization is a policy-gradient reinforcement-learning algorithm that limits policy updates to keep successive policies close and promote stable learning.
• Kullback–Leibler divergenceuses · Kullback–Leibler divergence is a nonnegative, directed measure of the information lost when one probability distribution approximates another, computed as an expected log ratio under the first distribution.
• PPO-clipalternative to · PPO-clip is a proximal policy optimization variant that clips the policy probability ratio in a surrogate objective to limit update magnitude.

This explainer was generated by Manning's knowledge graph from the text of Optimization Algorithms: AI techniques for design, planning, and control problems. The section links point to the parts of the book it draws on.