activity
20162025
most citedAsynchronous Methods for Deep Reinforcement Learning

1.7k citations · 2.7k across the 11 of their papers we have counts for

collaborators

11 papers

cs.AI2025

SIMA 2: A Generalist Embodied Agent for Virtual Worlds

SIMA team, Adrian Bolton, Alexander Lerchner +63

We introduce SIMA 2, a generalist embodied agent that understands and acts in a wide variety of 3D virtual worlds. Built upon a Gemini foundation model, SIMA 2 represents a signifi…

cs.RO2024★ 9 cited

Scaling Instructable Agents Across Many Simulated Worlds

SIMA Team, Maria Abi Raad, Arun Ahuja +91

Building embodied AI systems that can follow arbitrary language instructions in any 3D environment is a key challenge for creating general AI. Accomplishing this goal requires lear…

cond-mat.stat-mech2021★ 24 cited

Dynamical crossover behavior in the relaxation of quenched quantum many-body systems

Aamir Ahmad Makki, Souvik Bandyopadhyay, Somnath Maity +1

A crossover between different power-law relaxation behaviors of many-body periodically driven integrable systems has come to light in recent years. We demonstrate using integrable…

cs.LG2021★ 43 cited

Imitating Interactive Intelligence

Josh Abramson, Arun Ahuja, Iain Barr +26

A common vision from science fiction is that robots will one day inhabit our physical spaces, sense the world as we do, assist our physical labours, and communicate with us through…

cs.CL2020★ 32 cited

Human Instruction-Following with Deep Reinforcement Learning via Transfer-Learning from Text

Felix Hill, Sona Mokra, Nathaniel Wong +1

Recent work has described neural-network-based agents that are trained with reinforcement learning (RL) to execute language-like commands in simulated worlds, as a step towards an…

cs.AI2019★ 5 cited

A Generalized Framework for Population Based Training

Ang Li, Aleksandra Spyra, Sagi Perel +6

Population Based Training (PBT) is a recent approach that jointly optimizes neural network weights and hyperparameters which periodically copies weights of the best performers and…