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
5 Pith papers cite this work. Polarity classification is still indexing.
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IBAL framework constructs information-theoretic adversarial attacks on agent observations and actions to train MARL agents that remain robust to interaction disruptions and agent-missing scenarios.
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.
Introduces the game changer problem and supplies feasibility characterizations plus dynamic programming algorithms for forcing a target equilibrium under discrete reward constraints in two-player games.
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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Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning
IBAL framework constructs information-theoretic adversarial attacks on agent observations and actions to train MARL agents that remain robust to interaction disruptions and agent-missing scenarios.
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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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The Game Changer Problem: Controlling Equilibria with Discrete Rewards
Introduces the game changer problem and supplies feasibility characterizations plus dynamic programming algorithms for forcing a target equilibrium under discrete reward constraints in two-player games.