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New framework for extended mean field control using reinforcement learning

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel model-free reinforcement learning framework for continuous-time extended mean field control problems. This approach utilizes deterministic feedback policies, which simplify optimization by directly inducing the state-action distribution. The framework establishes a model-free sensitivity formula for McKean-Vlasov dynamics and derives a deterministic policy gradient on the Wasserstein space. It incorporates local value and advantage-rate representations, leading to a policy gradient with both action and measure-derivative terms, and is implemented via a continuous-time deep deterministic policy gradient algorithm. AI

IMPACT This research introduces a new method for complex control problems, potentially impacting areas like robotics and financial modeling.

RANK_REASON The cluster contains an academic paper detailing a new reinforcement learning framework.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New framework for extended mean field control using reinforcement learning

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The cluster contains an academic paper detailing a new reinforcement learning framework.
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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Ziheng Cheng, Xin Guo, Huy\^en Pham, Yufei Zhang ·

    Actor-Critic Learning for Extended Mean Field Control with Deterministic Policies

    arXiv:2607.11005v1 Announce Type: cross Abstract: This paper develops a model-free reinforcement learning framework for continuous--time extended mean field control problems, where both the dynamics and reward may depend on the joint distribution of states and controls. We adopt …

  2. arXiv stat.ML TIER_1 English(EN) · Yufei Zhang ·

    Actor-Critic Learning for Extended Mean Field Control with Deterministic Policies

    This paper develops a model-free reinforcement learning framework for continuous--time extended mean field control problems, where both the dynamics and reward may depend on the joint distribution of states and controls. We adopt deterministic feedback policies, under which the s…

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