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
Benchmarks
These leaderboards are used to track progress in Safe Exploration
Most implemented papers
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.
AI Safety Gridworlds
deepmind/ai-safety-gridworlds
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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
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
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
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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
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
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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
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
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
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.
