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Found 3 papers, 1 papers with code
Symbolic Imitation Learning: From Black-Box to Explainable Driving Policies
Current methods of imitation learning (IL), primarily based on deep neural networks, offer efficient means for obtaining driving policies from real-world data but suffer from significant limitations in interpretability and generalizability.
Towards Safe Autonomous Driving Policies using a Neuro-Symbolic Deep Reinforcement Learning Approach
The dynamic nature of driving environments and the presence of diverse road users pose significant challenges for decision-making in autonomous driving.
Self-Tuning PID Control via a Hybrid Actor-Critic-Based Neural Structure for Quadcopter Control
To tune dynamic gains, a model-free actor-critic-based hybrid neural structure was used that was able to properly tune PID gains, and also has done the best as an identifier.
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