English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper proposes a novel multi-agent reinforcement learning (MARL) methodto learn multiple coordinated agents under directed acyclic graph (DAG)constraints. Unlike existing MARL approaches, our method explicitly exploitsthe DAG structure between agents to achieve more effective learningperformance. Theoretically, we propose a novel surrogate value function basedon a MARL model with synthetic rewards (MARLM-SR) and prove that it serves as alower bound of the optimal value function. Computationally, we propose apractical training algorithm that exploits new notion of leader agent andreward generator and distributor agent to guide the decomposed follower agentsto better explore the parameter space in environments with DAG constraints.Empirically, we exploit four DAG environments including a real-world schedulingfor one of Intel's high volume packaging and test factory to benchmark ourmethods and show it outperforms the other non-DAG approaches.

Learning Multiple Coordinated Agents under Directed Acyclic Graph Constraints