Offline safe reinforcement learning (RL) algorithms promise to learn policies that satisfy
safety constraints directly in offline datasets without interacting with the environment. This …

The advancement of function-calling agent models requires diverse, reliable, and high-
quality datasets. This paper presents APIGen, an automated data generation pipeline …

Safe reinforcement learning (RL) focuses on training reward-maximizing agents subject to
pre-defined safety constraints. Yet, learning versatile safe policies that can adapt to varying …

Multi-constraint offline reinforcement learning (RL) promises to learn policies that satisfy
both cumulative and state-wise costs from offline datasets. This arrangement provides an …

We present a novel observation about the behavior of offline reinforcement learning (RL)
algorithms: on many benchmark datasets, offline RL can produce well-performing and safe …

Safe offline RL is a promising way to bypass risky online interactions towards safe policy
learning. Most existing methods only enforce soft constraints, ie, constraining safety …

Ensuring safety is critical when applying reinforcement learning (RL) to real-world problems.
Consequently, safe RL emerges as a fundamental and powerful paradigm for safely …

Offline constrained reinforcement learning (RL) aims to learn a policy that maximizes the
expected cumulative reward subject to constraints on expected cumulative cost using an …

In the domain of autonomous driving, the offline Reinforcement Learning (RL) approaches
exhibit notable efficacy in addressing sequential decision-making problems from offline …

Offline safe reinforcement learning (RL) aims to train a policy that satisfies constraints using
a pre-collected dataset. Most current methods struggle with the mismatch between imperfect …