Roberta Raileanu | DeepAI

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∙ 03/06/2023

MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning

Open-ended learning methods that automatically generate a curriculum of ...

0 Mikayel Samvelyan, et al. ∙

∙ 02/15/2023

Augmented Language Models: a Survey

This survey reviews works in which language models (LMs) are augmented w...

0 Grégoire Mialon, et al. ∙

∙ 02/09/2023

Toolformer: Language Models Can Teach Themselves to Use Tools

Language models (LMs) exhibit remarkable abilities to solve new tasks fr...

1 Timo Schick, et al. ∙

∙ 11/18/2022

Building a Subspace of Policies for Scalable Continual Learning

The ability to continuously acquire new knowledge and skills is crucial ...

0 Jean-Baptiste Gaya, et al. ∙

∙ 11/01/2022

Dungeons and Data: A Large-Scale NetHack Dataset

Recent breakthroughs in the development of agents to solve challenging s...

0 Eric Hambro, et al. ∙

∙ 10/11/2022

Exploration via Elliptical Episodic Bonuses

In recent years, a number of reinforcement learning (RL) methods have be...

0 Mikael Henaff, et al. ∙

∙ 03/22/2022

Insights From the NeurIPS 2021 NetHack Challenge

In this report, we summarize the takeaways from the first NeurIPS 2021 N...

9 Eric Hambro, et al. ∙

∙ 02/17/2022

Improving Intrinsic Exploration with Language Abstractions

Reinforcement learning (RL) agents are particularly hard to train when r...

5 Jesse Mu, et al. ∙

∙ 07/27/2021

Open-Ended Learning Leads to Generally Capable Agents

In this work we create agents that can perform well beyond a single, ind...

2 Open-Ended Learning Team, et al. ∙

∙ 02/20/2021

Decoupling Value and Policy for Generalization in Reinforcement Learning

Standard deep reinforcement learning algorithms use a shared representat...

0 Roberta Raileanu, et al. ∙

∙ 07/06/2020

Fast Adaptation via Policy-Dynamics Value Functions

Standard RL algorithms assume fixed environment dynamics and require a s...

12 Roberta Raileanu, et al. ∙

∙ 06/24/2020

The NetHack Learning Environment

Progress in Reinforcement Learning (RL) algorithms goes hand-in-hand wit...

33 Heinrich Küttler, et al. ∙

∙ 06/23/2020

Automatic Data Augmentation for Generalization in Deep Reinforcement Learning

Deep reinforcement learning (RL) agents often fail to generalize to unse...

0 Roberta Raileanu, et al. ∙

∙ 06/22/2020

Learning with AMIGo: Adversarially Motivated Intrinsic Goals

A key challenge for reinforcement learning (RL) consists of learning in ...

22 Andres Campero, et al. ∙

∙ 02/27/2020

RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments

Exploration in sparse reward environments remains one of the key challen...

0 Roberta Raileanu, et al. ∙

∙ 07/18/2018

Backplay: "Man muss immer umkehren"

A long-standing problem in model free reinforcement learning (RL) is tha...

2 Cinjon Resnick, et al. ∙

∙ 02/26/2018

Modeling Others using Oneself in Multi-Agent Reinforcement Learning

We consider the multi-agent reinforcement learning setting with imperfec...

0 Roberta Raileanu, et al. ∙