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We instantiate both inside a single CT-MARL benchmark (Poisson-clocked price updates, observation latency $\\delta$, interior-optimum logit demand), show that synchronous DDPG agents reliably trigger Failure Mode 1 with collusion index $\\Delta = 0.69 \\pm 0.11$, and quantify a partial microstructure fix: asynchrony alone cuts collusion by 48\\% and adding latency drives it to a minimum of $\\Delta = 0.28$. The fix has clearly documented costs: it is partial ($\\Delta$ remains supra-Bertrand), it is non-monotone in $\\delta$, and it does not survive Failure Mode 2, which emerges as DDPG critic divergence at $\\lambda = 5$ and corrupts the phase-diagram cell at $(\\lambda{=}5, \\delta{=}1)$. We accompany the scalar co","title":"Failure Modes of Deep Multi-Agent RL in Asynchronous Pricing: Reproducible Triggers, Trace Diagnostics, and a Partial Fix","url":"https://arxiv.org/abs/2606.09884","vendor":"arxiv_cs_ai"},"summary":"arXiv:2606.09884v1 Announce Type: cross \nAbstract: We study two reproducible failure modes of deep multi-agent reinforcement learning in continuous-time pricing markets: (i) tacit cartel formation between competing DDPG agents, and (ii) actor--critic instability at high event rates. We instantiate both inside a single CT-MARL benchmark (Poisson-clocked price updates, observation latency $\\delta$, interior-optimum logit demand), show that synchronous DDPG agents reliably trigger Failure Mode 1 with collusion index $\\Delta = 0.69 \\pm 0.11$, and quantify a partial microstructure fix: asynchrony alone cuts collusion by 48\\% and adding latency drives it to a minimum of $\\Delta = 0.28$. The fix has clearly documented costs: it is partial ($\\Delta$ remains supra-Bertrand), it is non-monotone in $\\delta$, and it does not survive Failure Mode 2, which emerges as DDPG critic divergence at $\\lambda = 5$ and corrupts the phase-diagram cell at $(\\lambda{=}5, \\delta{=}1)$. 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