4 citations · 6 across the 4 of their papers we have counts for
4 papers
A Survey of Constraint Formulations in Safe Reinforcement Learning
Akifumi Wachi, Xun Shen, Yanan Sui
Safety is critical when applying reinforcement learning (RL) to real-world problems. As a result, safe RL has emerged as a fundamental and powerful paradigm for optimizing an agent…
Long-term Safe Reinforcement Learning with Binary Feedback
Akifumi Wachi, Wataru Hashimoto, Kazumune Hashimoto
Safety is an indispensable requirement for applying reinforcement learning (RL) to real problems. Although there has been a surge of safe RL algorithms proposed in recent years, mo…
Safe Exploration in Reinforcement Learning: A Generalized Formulation and Algorithms
Akifumi Wachi, Wataru Hashimoto, Xun Shen +1
Safe exploration is essential for the practical use of reinforcement learning (RL) in many real-world scenarios. In this paper, we present a generalized safe exploration (GSE) prob…
Bayesian Meta-Learning on Control Barrier Functions with Data from On-Board Sensors
Wataru Hashimoto, Kazumune Hashimoto, Akifumi Wachi +3
In this paper, we consider a way to safely navigate the robots in unknown environments using measurement data from sensory devices. The control barrier function (CBF) is one of the…