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Found 5 papers, 4 papers with code
Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Model
Interestingly, we discover that via reachability analysis of safe-control theory, the hard safety constraint can be equivalently translated to identifying the largest feasible region given the offline dataset.
Safe Model-Based Reinforcement Learning with an Uncertainty-Aware Reachability Certificate
Furthermore, we build a safe RL framework to resolve constraints required by the DRC and its corresponding shield policy.
Model-based Reinforcement Learning
reinforcement-learning
+2
Reachability Constrained Reinforcement Learning
Recent studies incorporate feasible sets into CRL with energy-based methods such as control barrier function (CBF), safety index (SI), and leverage prior conservative estimations of feasible sets, which harms the performance of the learned policy.
Rethinking Pretraining as a Bridge from ANNs to SNNs
In this work, a new SNN training paradigm is proposed by combining the concepts of the two different training methods with the help of the pretrain technique and BP-based deep SNN training mechanism.
Self-learned Intelligence for Integrated Decision and Control of Automated Vehicles at Signalized Intersections
This paper develops the dynamic permutation state representation in the framework of integrated decision and control (IDC) to handle signalized intersections with mixed traffic flows.
