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In the context of safe exploration, Reinforcement Learning (RL) has long grappled with the challenges of balancing the tradeoff between maximizing rewards and minimizing safety violations, particularly in complex environments with contact-rich or non-smooth dynamics, and when dealing with high-dimensional pixel observations.
Learning safe, generalizable perception-based hybrid control with certificates
Charles Dawson, Bethany Lowenkamp, Dylan Goff, and Chuchu Fan · 1911
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A linear programming relaxation based approach for generating barrier certificates of hybrid systems
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Reinforcement learning: An introduction
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End-to-end safe reinforcement learning through barrier functions for safety-critical continuous control tasks
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Deep-reinforcement-learning-based autonomous voltage control for power grid operations
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Learning latent dynamics for planning from pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 2019
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Benchmarking Safe Exploration in Deep Reinforcement Learning
Alex Ray, Joshua Achiam, and Dario Amodei · 2019
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Constrained policy optimization via bayesian world models
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Learning observation-based certifiable safe policy for decentralized multi-robot navigation
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Constrained Markov decision processes
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Deep reinforcement learning for autonomous driving: A survey
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Learning safe multi-agent control with decentralized neural barrier certificates
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Offline reinforcement learning from images with latent space models
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Safe reinforcement learning from pixels using a stochastic latent representation
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Penalized proximal policy optimization for safe reinforcement learning
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Safe control with learned certificates: A survey of neural lyapunov, barrier, and contraction methods for robotics and control
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