2 citations · 2 across the 9 of their papers we have counts for
9 papers
Rethinking the Design of Reinforcement Learning-Based Deep Research Agents
Yi Wan, Jiuqi Wang, Liam Li +3
Large language models (LLMs) augmented with external tools are increasingly deployed as deep research agents that gather, reason over, and synthesize web information to answer comp…
Improving Generative Ad Text on Facebook using Reinforcement Learning
Daniel R. Jiang, Alex Nikulkov, Yu-Chia Chen +2
Generative artificial intelligence (AI), in particular large language models (LLMs), is poised to drive transformative economic change. LLMs are pre-trained on vast text data to le…
Aligned Multi Objective Optimization
Yonathan Efroni, Ben Kretzu, Daniel Jiang +4
To date, the multi-objective optimization literature has mainly focused on conflicting objectives, studying the Pareto front, or requiring users to balance tradeoffs. Yet, in machi…
An Empirical Study of Deep Reinforcement Learning in Continuing Tasks
Yi Wan, Dmytro Korenkevych, Zheqing Zhu
In reinforcement learning (RL), continuing tasks refer to tasks where the agent-environment interaction is ongoing and can not be broken down into episodes. These tasks are suitabl…
Epinet for Content Cold Start
Hong Jun Jeon, Songbin Liu, Yuantong Li +5
The exploding popularity of online content and its user base poses an evermore challenging matching problem for modern recommendation systems. Unlike other frontiers of machine lea…
Exploiting Structure in Offline Multi-Agent RL: The Benefits of Low Interaction Rank
Wenhao Zhan, Scott Fujimoto, Zheqing Zhu +3
We study the problem of learning an approximate equilibrium in the offline multi-agent reinforcement learning (MARL) setting. We introduce a structural assumption -- the interactio…
Uncertainty of Joint Neural Contextual Bandit
Hongbo Guo, Zheqing Zhu
Contextual bandit learning is increasingly favored in modern large-scale recommendation systems. To better utlize the contextual information and available user or item features, th…
Pearl: A Production-ready Reinforcement Learning Agent
Zheqing Zhu, Rodrigo de Salvo Braz, Jalaj Bhandari +12
Reinforcement learning (RL) is a versatile framework for optimizing long-term goals. Although many real-world problems can be formalized with RL, learning and deploying a performan…
Offline Reinforcement Learning for Optimizing Production Bidding Policies
Dmytro Korenkevych, Frank Cheng, Artsiom Balakir +5
The online advertising market, with its thousands of auctions run per second, presents a daunting challenge for advertisers who wish to optimize their spend under a budget constrai…