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PPO-Clip Attains Global Optimality: Towards Deeper Understandings of Clipping

AAAI 2024 · Optimization › Nonconvex and smooth optimization · approach: Coordinate and Adam convergence analysis · 22 citations (OpenAlex)

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Establishes the first global convergence guarantees for a PPO-Clip variant, tabular (asymptotic, via EMDA) and neural (O(1/sqrt(T)) min-iterate rate), by reinterpreting PPO-Clip as a hinge-loss–weighted objective.

Problem

Whether PPO-Clip, a widely used policy optimization algorithm, enjoys provable global convergence or convergence rate guarantees, and how the clipping mechanism affects convergence.

Approach

Reinterpret the PPO-Clip gradient as the negative gradient of an advantage-magnitude-weighted hinge loss, generalize it to other classifiers, and analyze it via entropic mirror descent (EMDA) with a two-step scheme: EMDA target policy, then regression-based neural approximation of that target.

Key results

• Neural PPO-Clip achieves an O(1/sqrt(T)) min-iterate convergence rate under neural function approximation, the best rate obtainable under the paper's general classifier conditions.
• Tabular PPO-Clip with direct parameterization is shown to converge asymptotically to the optimal value with probability one.
• Variants of Neural PPO-Clip with different classifiers achieve performance comparable or better than A2C and Rainbow on MinAtar and OpenAI Gym.

Surprising findings

• The clipping range epsilon affects only the pre-constant of the convergence rate, while the EMDA step size determines the rate, contrary to the intuition from PPO-KL's penalty parameter.

Limitations

• Analysis restricted to discrete action spaces.
• Neural analysis relies on wide networks (large width) and NTK-style linearization assumptions.
• Empirical evaluation is limited to standard small benchmarks compared against A2C and Rainbow.

Experimental setup

Datasets: MinAtar OpenAI Gym

Benchmarks: MinAtar OpenAI Gym

Metrics: training return

Baselines: A2C Rainbow

Builds on: PPO TRPO entropic mirror descent neural policy optimization analysis

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