Proposes hierarchical MARL framework enforcing safety via constraint manifold at low level with theoretical guarantees and stationary dynamics for stable training and generalization.
arXiv preprint arXiv:2110.02793 (2021)
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
TRIDENT is a MARL framework using Richardson-Romberg gradient correction, Lyapunov-constrained trust-region updates, and a physics-informed residual critic that claims O(1/sqrt(K)) convergence to constrained Nash equilibrium with O(sqrt(K)) violation bounds and large reductions in training violation
Phi-Actor-Critic is a new method that steers multi-agent reinforcement learning toward Pareto-efficient correlated equilibria using regret minimization and Lagrangian selection.
Proposes a clipping objective for sequential trust-region updates in independent-actor cooperative MARL that yields a monotonic improvement bound and sub-linear convergence to epsilon-Nash equilibria while reducing advantage variance.
citing papers explorer
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Safe and Generalizable Hierarchical Multi-Agent RL via Constraint Manifold Control
Proposes hierarchical MARL framework enforcing safety via constraint manifold at low level with theoretical guarantees and stationary dynamics for stable training and generalization.
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TRIDENT: Breaking the Hybrid-Safety-Physics Coupling for Provably Safe Multi-Agent Reinforcement Learning
TRIDENT is a MARL framework using Richardson-Romberg gradient correction, Lyapunov-constrained trust-region updates, and a physics-informed residual critic that claims O(1/sqrt(K)) convergence to constrained Nash equilibrium with O(sqrt(K)) violation bounds and large reductions in training violation
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Phi-Actor-Critic: Steering General-Sum Games to Pareto-Efficient Correlated Equilibria
Phi-Actor-Critic is a new method that steers multi-agent reinforcement learning toward Pareto-efficient correlated equilibria using regret minimization and Lagrangian selection.
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Low Variance Trust Region Optimization with Independent Actors and Sequential Updates in Cooperative Multi-agent Reinforcement Learning
Proposes a clipping objective for sequential trust-region updates in independent-actor cooperative MARL that yields a monotonic improvement bound and sub-linear convergence to epsilon-Nash equilibria while reducing advantage variance.