Abstract Context Machine Learning (ML) has been at the heart of many innovations over the
past years. However, including it in so-called “safety-critical” systems such as automotive or …

Safety remains a central obstacle preventing widespread use of RL in the real world:
learning new tasks in uncertain environments requires extensive exploration, but safety …

Many potential applications of reinforcement learning (RL) require guarantees that the agent
will perform well in the face of disturbances to the dynamics or reward function. In this paper …

Abstract Out-of-training-distribution (OOD) scenarios are a common challenge of learning
agents at deployment, typically leading to arbitrary deductions and poorly-informed …

Safety is an essential component for deploying reinforcement learning (RL) algorithms in
real-world scenarios, and is critical during the learning process itself. A natural first approach …

Safe exploration is regarded as a key priority area for reinforcement learning research. With
separate reward and safety signals, it is natural to cast it as constrained reinforcement …

Many reinforcement learning (RL) problems in practice are offline, learning purely from
observational data. A key challenge is how to ensure the learned policy is safe, which …

While reinforcement learning algorithms can learn effective policies for complex tasks, these
policies are often brittle to even minor task variations, especially when variations are not …

Safety is critical to broadening the real-world use of reinforcement learning. Modeling the
safety aspects using a safety-cost signal separate from the reward and bounding the …

In risk-averse reinforcement learning (RL), the goal is to optimize some risk measure of the
returns. A risk measure often focuses on the worst returns out of the agent's experience. As a …