May 3, 2024Open Access

Learning Optimal Deterministic Policies with Stochastic Policy Gradients

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Stochastic policy gradient methods can achieve global convergence to optimal deterministic policies in continuous reinforcement learning systems under gradient domination assumptions.
Tuning exploration levels directly optimizes the trade-off between sample complexity during learning and final deployed deterministic policy performance across control benchmarks.
Theoretical modeling and quantitative comparisons between action-based and parameter-based exploration provide formal foundations for deploying safe deterministic controllers.

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Implication

Theoretical analysis reveals global convergence of stochastic policy gradients to optimal deterministic policies, highlighting trade-offs between exploration level and sample complexity.

Cite This Study

Montenegro et al. (2024) studied this question.

synapsesocial.com/papers/68e6bbccb6db64358763c4a9 https://doi.org/10.48550/arxiv.2405.02235

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Implication

Theoretical analysis reveals global convergence of stochastic policy gradients to optimal deterministic policies, highlighting trade-offs between exploration level and sample complexity.

Learning Optimal Deterministic Policies with Stochastic Policy Gradients

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Discussion

Implication

Cite This Study

Learning Optimal Deterministic Policies with Stochastic Policy Gradients

Key Points

Discussion

Implication

Cite This Study

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