ParallelCBF is a composable framework that unifies tensor-parallel UAV environments, hard-gate CBF safety filters, sharded BC-to-RL pipelines, and operational auditability as first-class APIs for safe reinforcement learning.
A reduction of imitation learning and structured prediction to no-regret online learning
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
The paper delivers a concise, self-contained tutorial on foundational DRL algorithms including REINFORCE and PPO and DIL methods including behavioral cloning, DAgger, and GAIL for embodied agents.
citing papers explorer
-
parallelcbf: A composable safety-filter and auditability framework for tensor-parallel reinforcement learning
ParallelCBF is a composable framework that unifies tensor-parallel UAV environments, hard-gate CBF safety filters, sharded BC-to-RL pipelines, and operational auditability as first-class APIs for safe reinforcement learning.
-
An Introduction to Deep Reinforcement and Imitation Learning
The paper delivers a concise, self-contained tutorial on foundational DRL algorithms including REINFORCE and PPO and DIL methods including behavioral cloning, DAgger, and GAIL for embodied agents.