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Found 14 papers, 10 papers with code
Safe Guaranteed Exploration for Non-linear Systems
Based on this framework we propose an efficient algorithm, SageMPC, SAfe Guaranteed Exploration using Model Predictive Control.
Near-Optimal Multi-Agent Learning for Safe Coverage Control
In this paper, we aim to efficiently learn the density to approximately solve the coverage problem while preserving the agents' safety.
GoSafeOpt: Scalable Safe Exploration for Global Optimization of Dynamical Systems
Learning optimal control policies directly on physical systems is challenging since even a single failure can lead to costly hardware damage.
GoSafe: Globally Optimal Safe Robot Learning
When learning policies for robotic systems from data, safety is a major concern, as violation of safety constraints may cause hardware damage.
Information Directed Reward Learning for Reinforcement Learning
For many reinforcement learning (RL) applications, specifying a reward is difficult.
Safe and Efficient Model-free Adaptive Control via Bayesian Optimization
Thus, our approach builds on GoOSE, an algorithm for safe and sample-efficient Bayesian optimization.
Safe Reinforcement Learning via Curriculum Induction
In safety-critical applications, autonomous agents may need to learn in an environment where mistakes can be very costly.
Safe Exploration for Interactive Machine Learning
Existing algorithms for this problem learn about the safety of all decisions to ensure convergence.
Robust Model-free Reinforcement Learning with Multi-objective Bayesian Optimization
In reinforcement learning (RL), an autonomous agent learns to perform complex tasks by maximizing an exogenous reward signal while interacting with its environment.
Mixed-Variable Bayesian Optimization
However, few methods exist for mixed-variable domains and none of them can handle discrete constraints that arise in many real-world applications.
Learning-based Model Predictive Control for Safe Exploration and Reinforcement Learning
We evaluate the resulting algorithm to safely explore the dynamics of an inverted pendulum and to solve a reinforcement learning task on a cart-pole system with safety constraints.
Learning-based Model Predictive Control for Safe Exploration
However, these methods typically do not provide any safety guarantees, which prevents their use in safety-critical, real-world applications.
Safe Model-based Reinforcement Learning with Stability Guarantees
Reinforcement learning is a powerful paradigm for learning optimal policies from experimental data.
Model-based Reinforcement Learning
reinforcement-learning
+1
Safe Exploration in Finite Markov Decision Processes with Gaussian Processes
We define safety in terms of an, a priori unknown, safety constraint that depends on states and actions.
