301 citations · 439 across the 27 of their papers we have counts for
7 papers · 1 filter
General Intelligence Requires Rethinking Exploration
Minqi Jiang, Tim Rocktäschel, Edward Grefenstette
We are at the cusp of a transition from "learning from data" to "learning what data to learn from" as a central focus of artificial intelligence (AI) research. While the first-orde…
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…
GriddlyJS: A Web IDE for Reinforcement Learning
Christopher Bamford, Minqi Jiang, Mikayel Samvelyan +1
Progress in reinforcement learning (RL) research is often driven by the design of new, challenging environments -- a costly undertaking requiring skills orthogonal to that of a typ…
Grounding Aleatoric Uncertainty for Unsupervised Environment Design
Minqi Jiang, Michael Dennis, Jack Parker-Holder +5
Adaptive curricula in reinforcement learning (RL) have proven effective for producing policies robust to discrepancies between the train and test environment. Recently, the Unsuper…
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…
Evolving Curricula with Regret-Based Environment Design
Jack Parker-Holder, Minqi Jiang, Michael Dennis +4
It remains a significant challenge to train generally capable agents with reinforcement learning (RL). A promising avenue for improving the robustness of RL agents is through the u…
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…