65 citations · 364 across the 38 of their papers we have counts for
14 papers · 1 filter
Deterministic and Discriminative Imitation (D2-Imitation): Revisiting Adversarial Imitation for Sample Efficiency
Mingfei Sun, Sam Devlin, Katja Hofmann +1
Sample efficiency is crucial for imitation learning methods to be applicable in real-world applications. Many studies improve sample efficiency by extending adversarial imitation t…
NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environment
Julia Kiseleva, Ziming Li, Mohammad Aliannejadi +12
Human intelligence has the remarkable ability to adapt to new tasks and environments quickly. Starting from a very young age, humans acquire new skills and learn how to solve new t…
SocialAI: Benchmarking Socio-Cognitive Abilities in Deep Reinforcement Learning Agents
Grgur Kovač, Rémy Portelas, Katja Hofmann +1
Building embodied autonomous agents capable of participating in social interactions with humans is one of the main challenges in AI. Within the Deep Reinforcement Learning (DRL) fi…
Strategically Efficient Exploration in Competitive Multi-agent Reinforcement Learning
Robert Loftin, Aadirupa Saha, Sam Devlin +1
High sample complexity remains a barrier to the application of reinforcement learning (RL), particularly in multi-agent systems. A large body of work has demonstrated that explorat…
Memory Efficient Meta-Learning with Large Images
John Bronskill, Daniela Massiceti, Massimiliano Patacchiola +3
Meta learning approaches to few-shot classification are computationally efficient at test time, requiring just a few optimization steps or single forward pass to learn a new task,…
SoftDICE for Imitation Learning: Rethinking Off-policy Distribution Matching
Mingfei Sun, Anuj Mahajan, Katja Hofmann +1
We present SoftDICE, which achieves state-of-the-art performance for imitation learning. SoftDICE fixes several key problems in ValueDICE, an off-policy distribution matching appro…
Grounding Spatio-Temporal Language with Transformers
Tristan Karch, Laetitia Teodorescu, Katja Hofmann +2
Language is an interface to the outside world. In order for embodied agents to use it, language must be grounded in other, sensorimotor modalities. While there is an extended liter…
Learning to Win, Lose and Cooperate through Reward Signal Evolution
Rafal Muszynski, Katja Hofmann, Jun Wang
Solving a reinforcement learning problem typically involves correctly prespecifying the reward signal from which the algorithm learns. Here, we approach the problem of reward signa…
Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation
Sam Devlin, Raluca Georgescu, Ida Momennejad +6
A key challenge on the path to developing agents that learn complex human-like behavior is the need to quickly and accurately quantify human-likeness. While human assessments of su…
SocialAI 0.1: Towards a Benchmark to Stimulate Research on Socio-Cognitive Abilities in Deep Reinforcement Learning Agents
Grgur Kovač, Rémy Portelas, Katja Hofmann +1
Building embodied autonomous agents capable of participating in social interactions with humans is one of the main challenges in AI. This problem motivated many research directions…
ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition
Daniela Massiceti, Luisa Zintgraf, John Bronskill +6
Object recognition has made great advances in the last decade, but predominately still relies on many high-quality training examples per object category. In contrast, learning new…
TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL
Clément Romac, Rémy Portelas, Katja Hofmann +1
Training autonomous agents able to generalize to multiple tasks is a key target of Deep Reinforcement Learning (DRL) research. In parallel to improving DRL algorithms themselves, A…
Evaluating the Robustness of Collaborative Agents
Paul Knott, Micah Carroll, Sam Devlin +4
In order for agents trained by deep reinforcement learning to work alongside humans in realistic settings, we will need to ensure that the agents are \emph{robust}. Since the real…
Deep Interactive Bayesian Reinforcement Learning via Meta-Learning
Luisa Zintgraf, Sam Devlin, Kamil Ciosek +2
Agents that interact with other agents often do not know a priori what the other agents' strategies are, but have to maximise their own online return while interacting with and lea…