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Distributed zeroth-order policy gradient for networked multi-agent reinforcement learning from human feedback (RLHF). Addresses scalability of preference-based RL to multi-agent systems without centralized training. Activation: distributed multi-agent RLHF, zeroth-order policy gradient, networked MARL, human feedback multi-agent, decentralized preference RL, spatial-temporal truncated trajectory.
distributed-zeroth-order-marlh is an agent skill entry in ChatHome Skills, sourced from GitHub Skill Search and scored 58/100. # Distributed Zeroth-Order Policy Gradient for Networked MARL with Human Feedback **Paper:** "Distributed Zeroth-Order Policy Gradient for Networked Multi-agent. As an agent skill, it packages a reusable instruction set that AI coding agents such as Codex and Claude Code can load to perform this task without hand-written glue code. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding distributed-zeroth-order-marlh to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
codex skill install hiyenwong/ai_collectionReview the upstream source before running this command.
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# Distributed Zeroth-Order Policy Gradient for Networked MARL with Human Feedback **Paper:** "Distributed Zeroth-Order Policy Gradient for Networked Multi-agent Reinforcement Learn