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Found 5 papers, 3 papers with code
Co-learning Planning and Control Policies Constrained by Differentiable Logic Specifications
Synthesizing planning and control policies in robotics is a fundamental task, further complicated by factors such as complex logic specifications and high-dimensional robot dynamics.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
DistSPECTRL: Distributing Specifications in Multi-Agent Reinforcement Learning Systems
While notable progress has been made in specifying and learning objectives for general cyber-physical systems, applying these methods to distributed multi-agent systems still pose significant challenges.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Defending Observation Attacks in Deep Reinforcement Learning via Detection and Denoising
We focus our attention on well-trained deterministic and stochastic neural network policies in the context of continuous control benchmarks subject to four well-studied observation space adversarial attacks.
Model-free Neural Lyapunov Control for Safe Robot Navigation
Model-free Deep Reinforcement Learning (DRL) controllers have demonstrated promising results on various challenging non-linear control tasks.
Robustness to Adversarial Attacks in Learning-Enabled Controllers
We consider shield-based defenses as a means to improve controller robustness in the face of such perturbations.
