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Found 4 papers, 1 papers with code
Learning Control Policies for Variable Objectives from Offline Data
Offline reinforcement learning provides a viable approach to obtain advanced control strategies for dynamical systems, in particular when direct interaction with the environment is not available.
A Benchmark Environment Motivated by Industrial Control Problems
On one hand, these benchmarks are designed to provide interpretable RL training scenarios and detailed insight into the learning process of the method on hand.
Batch Reinforcement Learning on the Industrial Benchmark: First Experiences
The Particle Swarm Optimization Policy (PSO-P) has been recently introduced and proven to produce remarkable results on interacting with academic reinforcement learning benchmarks in an off-policy, batch-based setting.
Introduction to the "Industrial Benchmark"
A novel reinforcement learning benchmark, called Industrial Benchmark, is introduced.
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