most citedTowards Improving Reward Design in RL: A Reward Alignment Metric for RL Practitioners

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cs.LG2025★ 1 cited

Towards Improving Reward Design in RL: A Reward Alignment Metric for RL Practitioners

Calarina Muslimani, Kerrick Johnstonbaugh, Suyog Chandramouli +3

Reinforcement learning agents are fundamentally limited by the quality of the reward functions they learn from, yet reward design is often overlooked under the assumption that a we…

cs.LG2024

MobileSafetyBench: Evaluating Safety of Autonomous Agents in Mobile Device Control

Juyong Lee, Dongyoon Hahm, June Suk Choi +2

Autonomous agents powered by large language models (LLMs) show promising potential in assistive tasks across various domains, including mobile device control. As these agents inter…