A litmus test based on reachset-conformant model identification and correlation analysis of uncertainties predicts if RL-based control is superior to model-based control without any RL training.
Benchmarking deep reinforcement learning for continuous control
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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OpenAI Gym introduces a common interface for reinforcement learning environments and a results-sharing website to enable consistent algorithm comparisons.
Pretraining Soft Actor-Critic agents via behavior cloning on PyWake-generated expert trajectories in WindGym simulations eliminates the initial learning phase for 2x2 wind farm control and yields final performance exceeding a lookup-table baseline.
citing papers explorer
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To Learn or Not to Learn: A Litmus Test for Using Reinforcement Learning in Control
A litmus test based on reachset-conformant model identification and correlation analysis of uncertainties predicts if RL-based control is superior to model-based control without any RL training.
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OpenAI Gym
OpenAI Gym introduces a common interface for reinforcement learning environments and a results-sharing website to enable consistent algorithm comparisons.
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Accelerating Reinforcement Learning for Wind Farm Control via Expert Demonstrations
Pretraining Soft Actor-Critic agents via behavior cloning on PyWake-generated expert trajectories in WindGym simulations eliminates the initial learning phase for 2x2 wind farm control and yields final performance exceeding a lookup-table baseline.