1 citations · 4 across the 7 of their papers we have counts for
7 papers
Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning
Lynn Cherif, Flemming Kondrup, David Venuto +3
Agents that can autonomously navigate the web through a graphical user interface (GUI) using a unified action space (e.g., mouse and keyboard actions) can require very large amount…
Command A: An Enterprise-Ready Large Language Model
Team Cohere, :, Aakanksha +227
In this report we describe the development of Command A, a powerful large language model purpose-built to excel at real-world enterprise use cases. Command A is an agent-optimised…
Code as Reward: Empowering Reinforcement Learning with VLMs
David Venuto, Sami Nur Islam, Martin Klissarov +3
Pre-trained Vision-Language Models (VLMs) are able to understand visual concepts, describe and decompose complex tasks into sub-tasks, and provide feedback on task completion. In t…
Multi-Environment Pretraining Enables Transfer to Action Limited Datasets
David Venuto, Sherry Yang, Pieter Abbeel +3
Using massive datasets to train large-scale models has emerged as a dominant approach for broad generalization in natural language and vision applications. In reinforcement learnin…
Policy Gradients Incorporating the Future
David Venuto, Elaine Lau, Doina Precup +1
Reasoning about the future -- understanding how decisions in the present time affect outcomes in the future -- is one of the central challenges for reinforcement learning (RL), esp…
oIRL: Robust Adversarial Inverse Reinforcement Learning with Temporally Extended Actions
David Venuto, Jhelum Chakravorty, Leonard Boussioux +3
Explicit engineering of reward functions for given environments has been a major hindrance to reinforcement learning methods. While Inverse Reinforcement Learning (IRL) is a soluti…
Avoidance Learning Using Observational Reinforcement Learning
David Venuto, Leonard Boussioux, Junhao Wang +4
Imitation learning seeks to learn an expert policy from sampled demonstrations. However, in the real world, it is often difficult to find a perfect expert and avoiding dangerous be…