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Found 3 papers, 2 papers with code
Active Measure Reinforcement Learning for Observation Cost Minimization
Our empirical evaluation demonstrates that Amrl-Q agents are able to learn a policy and state estimator in parallel during online training.
Reinforcement Learning in a Physics-Inspired Semi-Markov Environment
Reinforcement learning (RL) has been demonstrated to have great potential in many applications of scientific discovery and design.
Optimizing thermodynamic trajectories using evolutionary and gradient-based reinforcement learning
Gradient-based reinforcement learning is able to learn the Stirling cycle, whereas an evolutionary approach achieves the optimal Carnot cycle.
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