21 papers with code • 0 benchmarks • 0 datasets
Safe Exploration is an approach to collect ground truth data by safely interacting with the environment.
These leaderboards are used to track progress in Safe Exploration
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.
We further evaluate various safe reinforcement learning and multi-agent reinforcement learning algorithms in MetaDrive environments and provide the benchmarks.
However, these methods typically do not provide any safety guarantees, which prevents their use in safety-critical, real-world applications.
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.
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.