arXiv Machine Learning
Environment Parameter Gradient Theorem for Co-Design in Reinforcement Learning
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Environment Parameter Gradient Theorem for Policy-Environment Co-Design in Reinforcement Learning
arXiv:2607. 12590v1 Announce Type: cross Abstract: Reinforcement learning (RL) is traditionally concerned with learning a control policy for a fixed environment.
arXiv Machine Learning
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Differentiable Policy Transport over Multi-Layer Network Feasibility Geometry
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arXiv Machine Learning
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Trajectory Data Suffices for Statistically Efficient Policy Evaluation in Fixed-Horizon Offline RL with Linear $q^\pi$-Realizability and Concentrability
arXiv:2510. 03494v2 Announce Type: replace Abstract: We study finite-horizon offline reinforcement learning (RL) with function approximation for both policy evaluation and policy optimization.
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Diffusion Offline Reinforcement Learning for Fair and Energy-Efficient UAV-Assisted Wireless Networks
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A Multi-Agent system for Multi-Objective constrained optimization
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arXiv Machine Learning
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Multi-Agent Reinforcement Learning-Based UAV Pathfinding for Obstacle Avoidance in Stochastic Environment
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