D4PG: distributed deterministic actors with a distributional critic

What changed

Combined deterministic policy gradients with a distributional critic, distributed experience collection, multi-step targets and prioritized replay for continuous-control learning.

Scientific scope

Combined deterministic policy gradients with a distributional critic, distributed experience collection, multi-step targets and prioritized replay for continuous-control learning. A synthesis extending DDPG and distributional RL, not invention of their components. Reported robustness and benchmark gains are empirical, not general continuous-control optimality or safety.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. A synthesis extending DDPG and distributional RL, not invention of their components. Reported robustness and benchmark gains are empirical, not general continuous-control optimality or safety.

Contributors

• Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

• Distributed Distributional Deterministic Policy Gradients

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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