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Safe Exploration

Safe Exploration is an approach to collect ground truth data by safely interacting with the environment.

Source: Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems

Papers

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Showing 21–30 of 135 papers

TitleStatusHype
Safe Reinforcement Learning in Black-Box Environments via Adaptive ShieldingCode0
Preparing for Black Swans: The Antifragility Imperative for Machine Learning—0
Contextual Affordances for Safe Exploration in Robotic Scenarios—0
Safe Exploration Using Bayesian World Models and Log-Barrier Optimization—0
Information-Theoretic Safe Bayesian Optimization—0
Trajectory-wise Iterative Reinforcement Learning Framework for Auto-bidding—0
A comparison of RL-based and PID controllers for 6-DOF swimming robots: hybrid underwater object trackingCode0
Towards Socially and Morally Aware RL agent: Reward Design With LLM—0
Towards Safe Load Balancing based on Control Barrier Functions and Deep Reinforcement Learning—0
Learning Human-like Representations to Enable Learning Human Values—0
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