About
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
Benchmarks
Greatest papers with code
AI Safety Gridworlds
• deepmind/ai-safety-gridworlds
• 
We present a suite of reinforcement learning environments illustrating various safety properties of intelligent agents.
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.
Verifiably Safe Exploration for End-to-End Reinforcement Learning
We also prove that our method of enforcing the safety constraints preserves all safe policies from the original environment.
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.
Safe Exploration in Continuous Action Spaces
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.
SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference Measure
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.
Ranked #1 on
Classification
on XOR
DOMAIN ADAPTATION IMAGE CLASSIFICATION INTRUSION DETECTION SAFE EXPLORATION
Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution
Align-RUDDER outperforms competitors on complex artificial tasks with delayed reward and few demonstrations.
GENERAL REINFORCEMENT LEARNING MINECRAFT MULTIPLE SEQUENCE ALIGNMENT SAFE EXPLORATION
Provably Safe PAC-MDP Exploration Using Analogies
A key challenge in applying reinforcement learning to safety-critical domains is understanding how to balance exploration (needed to attain good performance on the task) with safety (needed to avoid catastrophic failure).
Neurosymbolic Reinforcement Learning with Formally Verified Exploration
We present Revel, a partially neural reinforcement learning (RL) framework for provably safe exploration in continuous state and action spaces.