Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …

Abstract Space mission design places a premium on cost and operational efficiency. The
search for new science and life beyond Earth calls for spacecraft that can deliver scientific …

Over the last decade, the use of autonomous drone systems for surveying, search and
rescue, or last-mile delivery has increased exponentially. With the rise of these applications …

In many real-world reinforcement learning (RL) problems, besides optimizing the main
objective function, an agent must concurrently avoid violating a number of constraints. In …

When learning policies for real-world domains, two important questions arise:(i) how to
efficiently use pre-collected off-policy, non-optimal behavior data; and (ii) how to mediate …

We study sequential decision-making problems in which each agent aims to maximize the
expected total reward while satisfying a constraint on the expected total utility. We employ …

In many sequential decision-making problems one is interested in minimizing an expected
cumulative cost while taking into account risk, ie, increased awareness of events of small …

Although ground robotic autonomy has gained widespread usage in structured and
controlled environments, autonomy in unknown and off-road terrain remains a difficult …

Endowing robots with the capability of assessing risk and making risk-aware decisions is
widely considered a key step toward ensuring safety for robots operating under uncertainty …

We present a reinforcement learning approach to explore and optimize a safety-constrained
Markov Decision Process (MDP). In this setting, the agent must maximize discounted …