Asim Munawar

Constrained Exploration and Recovery from Experience Shaping

2018 · arXiv (Cornell University)

We consider the problem of reinforcement learning under safety requirements, in which an agent is trained to complete a given task, typically formalized as the maximization of a reward signal over time, while concurrently avoiding …

NESTFUL: A Benchmark for Evaluating LLMs on Nested Sequences of API Calls

2024 · arXiv (Cornell University)

The resurgence of autonomous agents built using large language models (LLMs) to solve complex real-world tasks has brought increased focus on LLMs' fundamental ability of tool or function calling. At the core of these agents, …