48 citations · 56 across the 8 of their papers we have counts for
8 papers
Training Language Models for Social Deduction with Multi-Agent Reinforcement Learning
Bidipta Sarkar, Warren Xia, C. Karen Liu +1
Communicating in natural language is a powerful tool in multi-agent settings, as it enables independent agents to share information in partially observable settings and allows zero…
LOB-Bench: Benchmarking Generative AI for Finance -- an Application to Limit Order Book Data
Peer Nagy, Sascha Frey, Kang Li +5
While financial data presents one of the most challenging and interesting sequence modelling tasks due to high noise, heavy tails, and strategic interactions, progress in this area…
An Interactive Agent Foundation Model
Zane Durante, Bidipta Sarkar, Ran Gong +17
The development of artificial intelligence systems is transitioning from creating static, task-specific models to dynamic, agent-based systems capable of performing well in a wide…
Agent AI: Surveying the Horizons of Multimodal Interaction
Zane Durante, Qiuyuan Huang, Naoki Wake +11
Multi-modal AI systems will likely become a ubiquitous presence in our everyday lives. A promising approach to making these systems more interactive is to embody them as agents wit…
Diverse Conventions for Human-AI Collaboration
Bidipta Sarkar, Andy Shih, Dorsa Sadigh
Conventions are crucial for strong performance in cooperative multi-agent games, because they allow players to coordinate on a shared strategy without explicit communication. Unfor…
Physically Grounded Vision-Language Models for Robotic Manipulation
Jensen Gao, Bidipta Sarkar, Fei Xia +5
Recent advances in vision-language models (VLMs) have led to improved performance on tasks such as visual question answering and image captioning. Consequently, these models are no…
PantheonRL: A MARL Library for Dynamic Training Interactions
Bidipta Sarkar, Aditi Talati, Andy Shih +1
We present PantheonRL, a multiagent reinforcement learning software package for dynamic training interactions such as round-robin, adaptive, and ad-hoc training. Our package is des…
Gaussian Process Policy Optimization
Ashish Rao, Bidipta Sarkar, Tejas Narayanan
We propose a novel actor-critic, model-free reinforcement learning algorithm which employs a Bayesian method of parameter space exploration to solve environments. A Gaussian proces…