65 citations · 364 across the 38 of their papers we have counts for
40 papers
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation
Massimiliano Patacchiola, Mingfei Sun, Katja Hofmann +1
In this paper we explore few-shot imitation learning for control problems, which involves learning to imitate a target policy by accessing a limited set of offline rollouts. This s…
Learning Safety Constraints from Demonstrations with Unknown Rewards
David Lindner, Xin Chen, Sebastian Tschiatschek +2
We propose Convex Constraint Learning for Reinforcement Learning (CoCoRL), a novel approach for inferring shared constraints in a Constrained Markov Decision Process (CMDP) from a…
Imitating Human Behaviour with Diffusion Models
Tim Pearce, Tabish Rashid, Anssi Kanervisto +8
Diffusion models have emerged as powerful generative models in the text-to-image domain. This paper studies their application as observation-to-action models for imitating human be…
Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games
Stephanie Milani, Arthur Juliani, Ida Momennejad +7
We aim to understand how people assess human likeness in navigation produced by people and artificially intelligent (AI) agents in a video game. To this end, we propose a novel AI…
Trust-Region-Free Policy Optimization for Stochastic Policies
Mingfei Sun, Benjamin Ellis, Anuj Mahajan +3
Trust Region Policy Optimization (TRPO) is an iterative method that simultaneously maximizes a surrogate objective and enforces a trust region constraint over consecutive policies…
UniMASK: Unified Inference in Sequential Decision Problems
Micah Carroll, Orr Paradise, Jessy Lin +8
Randomly masking and predicting word tokens has been a successful approach in pre-training language models for a variety of downstream tasks. In this work, we observe that the same…
Interactively Learning Preference Constraints in Linear Bandits
David Lindner, Sebastian Tschiatschek, Katja Hofmann +1
We study sequential decision-making with known rewards and unknown constraints, motivated by situations where the constraints represent expensive-to-evaluate human preferences, suc…
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification
Massimiliano Patacchiola, John Bronskill, Aliaksandra Shysheya +3
Recent years have seen a growth in user-centric applications that require effective knowledge transfer across tasks in the low-data regime. An example is personalization, where a p…
Towards Flexible Inference in Sequential Decision Problems via Bidirectional Transformers
Micah Carroll, Jessy Lin, Orr Paradise +8
Randomly masking and predicting word tokens has been a successful approach in pre-training language models for a variety of downstream tasks. In this work, we observe that the same…
Trust Region Bounds for Decentralized PPO Under Non-stationarity
Mingfei Sun, Sam Devlin, Jacob Beck +2
We present trust region bounds for optimizing decentralized policies in cooperative Multi-Agent Reinforcement Learning (MARL), which holds even when the transition dynamics are non…
You May Not Need Ratio Clipping in PPO
Mingfei Sun, Vitaly Kurin, Guoqing Liu +4
Proximal Policy Optimization (PPO) methods learn a policy by iteratively performing multiple mini-batch optimization epochs of a surrogate objective with one set of sampled data. R…
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…