The work gives the first algorithms for general robust Markov games with linear function approximation whose sample complexity breaks the curse of multiagency for large state spaces in both generative and online settings.
Robust deep reinforcement learning against adversarial perturbations on state observations
4 Pith papers cite this work. Polarity classification is still indexing.
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RAPO uses a dual robust RL formulation with trajectory-level adversarial networks and model-level Boltzmann reweighting over dynamics ensembles to improve policy resilience and out-of-distribution generalization while keeping the problem tractable.
Wolfpack attack framework disrupts MARL cooperation by targeting initial and assisting agents; WALL trains robust policies against it with reported experimental gains.
citing papers explorer
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Taming the Curses of Multiagency in Robust Markov Games with Large State Space through Linear Function Approximation
The work gives the first algorithms for general robust Markov games with linear function approximation whose sample complexity breaks the curse of multiagency for large state spaces in both generative and online settings.
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Robust Adversarial Policy Optimization Under Dynamics Uncertainty
RAPO uses a dual robust RL formulation with trajectory-level adversarial networks and model-level Boltzmann reweighting over dynamics ensembles to improve policy resilience and out-of-distribution generalization while keeping the problem tractable.
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Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning
Wolfpack attack framework disrupts MARL cooperation by targeting initial and assisting agents; WALL trains robust policies against it with reported experimental gains.
- Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning