PP-DTD achieves linear convergence to a neighborhood of the optimum under constant step-sizes and O(T^{-1}) under decaying step-sizes for distributed TD policy evaluation in MARL over directed graphs, claimed as the first with rates comparable to single-agent TD.
Human-level control through deep reinforcement learning
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NaviSlim uses a gated slimmable architecture to dynamically scale neural model complexity and onboard sensor power for context-aware navigation in micro-drones, reporting 57-92% average model reduction and 61-80% sensor utilization in AirSim simulations versus static full-complexity baselines.
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Distributed TD Tracking with Linear Function Approximation over Directed Communication Networks
PP-DTD achieves linear convergence to a neighborhood of the optimum under constant step-sizes and O(T^{-1}) under decaying step-sizes for distributed TD policy evaluation in MARL over directed graphs, claimed as the first with rates comparable to single-agent TD.
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NaviSlim: Adaptive Context-Aware Navigation and Sensing via Dynamic Slimmable Networks
NaviSlim uses a gated slimmable architecture to dynamically scale neural model complexity and onboard sensor power for context-aware navigation in micro-drones, reporting 57-92% average model reduction and 61-80% sensor utilization in AirSim simulations versus static full-complexity baselines.