Safe Exploration

21 papers with code • 0 benchmarks • 0 datasets

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

Most implemented papers

Safe Exploration in Continuous Action Spaces

utiasDSL/safe-control-gym 26 Jan 2018

We address the problem of deploying a reinforcement learning (RL) agent on a physical system such as a datacenter cooling unit or robot, where critical constraints must never be violated.

AI Safety Gridworlds

deepmind/ai-safety-gridworlds 27 Nov 2017

We present a suite of reinforcement learning environments illustrating various safety properties of intelligent agents.

SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference Measure

ISorokos/SafeML 27 May 2020

Ensuring safety and explainability of machine learning (ML) is a topic of increasing relevance as data-driven applications venture into safety-critical application domains, traditionally committed to high safety standards that are not satisfied with an exclusive testing approach of otherwise inaccessible black-box systems.

MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning

metadriverse/metadrive 26 Sep 2021

We further evaluate various safe reinforcement learning and multi-agent reinforcement learning algorithms in MetaDrive environments and provide the benchmarks.

Safe Policy Optimization with Local Generalized Linear Function Approximations

maximecb/gym-minigrid NeurIPS 2021

Safe exploration is a key to applying reinforcement learning (RL) in safety-critical systems.

Safe Exploration in Finite Markov Decision Processes with Gaussian Processes

befelix/SafeMDP NeurIPS 2016

We define safety in terms of an, a priori unknown, safety constraint that depends on states and actions.

Concrete Problems in AI Safety

mateuszjurewicz/bornhack_ml_crashcourse 21 Jun 2016

Rapid progress in machine learning and artificial intelligence (AI) has brought increasing attention to the potential impacts of AI technologies on society.

Learning-based Model Predictive Control for Safe Exploration

befelix/safe-exploration 22 Mar 2018

However, these methods typically do not provide any safety guarantees, which prevents their use in safety-critical, real-world applications.

Learning-based Model Predictive Control for Safe Exploration and Reinforcement Learning

befelix/safe-exploration 27 Jun 2019

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.

Safe Exploration for Optimizing Contextual Bandits

rjagerman/tois2019-safe-exploration-algorithm 2 Feb 2020

Our experiments using text classification and document retrieval confirm the above by comparing SEA (and a boundless variant called BSEA) to online and offline learning methods for contextual bandit problems.