RL4F provides the first standardized offline RL benchmark for multi-actuator long-horizon plasma control using DIII-D data, with model-based methods showing best average performance across rotation, density, temperature, and pressure tasks.
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Single-loop actor-critic achieves the first Õ(ε^{-2}) sample complexity for ε-optimal policies under minimal irreducibility assumptions.
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Offline Reinforcement Learning for Plasma Control in Nuclear Fusion: Codebase and Benchmark
RL4F provides the first standardized offline RL benchmark for multi-actuator long-horizon plasma control using DIII-D data, with model-based methods showing best average performance across rotation, density, temperature, and pressure tasks.
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Achieving $\epsilon^{-2}$ Sample Complexity for Single-Loop Actor-Critic under Minimal Assumptions
Single-loop actor-critic achieves the first Õ(ε^{-2}) sample complexity for ε-optimal policies under minimal irreducibility assumptions.