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Found 2 papers, 2 papers with code
Physics-Informed Representation and Learning: Control and Risk Quantification
Optimal and safety-critical control are fundamental problems for stochastic systems, and are widely considered in real-world scenarios such as robotic manipulation and autonomous driving.
Benchmarking Actor-Critic Deep Reinforcement Learning Algorithms for Robotics Control with Action Constraints
This study presents a benchmark for evaluating action-constrained reinforcement learning (RL) algorithms.
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