400 citations · 1.3k across the 51 of their papers we have counts for
5 papers · 1 filter
Building a Subspace of Policies for Scalable Continual Learning
Jean-Baptiste Gaya, Thang Doan, Lucas Caccia +3
The ability to continuously acquire new knowledge and skills is crucial for autonomous agents. Existing methods are typically based on either fixed-size models that struggle to lea…
Dungeons and Data: A Large-Scale NetHack Dataset
Eric Hambro, Roberta Raileanu, Danielle Rothermel +4
Recent breakthroughs in the development of agents to solve challenging sequential decision making problems such as Go, StarCraft, or DOTA, have relied on both simulated environment…
Exploration via Elliptical Episodic Bonuses
Mikael Henaff, Roberta Raileanu, Minqi Jiang +1
In recent years, a number of reinforcement learning (RL) methods have been proposed to explore complex environments which differ across episodes. In this work, we show that the eff…
Insights From the NeurIPS 2021 NetHack Challenge
Eric Hambro, Sharada Mohanty, Dmitrii Babaev +26
In this report, we summarize the takeaways from the first NeurIPS 2021 NetHack Challenge. Participants were tasked with developing a program or agent that can win (i.e., 'ascend' i…
Improving Intrinsic Exploration with Language Abstractions
Jesse Mu, Victor Zhong, Roberta Raileanu +4
Reinforcement learning (RL) agents are particularly hard to train when rewards are sparse. One common solution is to use intrinsic rewards to encourage agents to explore their envi…