b'Katja Hofmann'

research

∙ 06/23/2023

Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation

In this paper we explore few-shot imitation learning for control problem...

0 Massimiliano Patacchiola, et al. ∙

research

∙ 05/25/2023

Learning Safety Constraints from Demonstrations with Unknown Rewards

We propose Convex Constraint Learning for Reinforcement Learning (CoCoRL...

0 David Lindner, et al. ∙

research

∙ 03/02/2023

Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games

We aim to understand how people assess human likeness in navigation prod...

0 Stephanie Milani, et al. ∙

research

∙ 02/15/2023

Trust-Region-Free Policy Optimization for Stochastic Policies

Trust Region Policy Optimization (TRPO) is an iterative method that simu...

0 Mingfei Sun, et al. ∙

research

∙ 01/25/2023

Imitating Human Behaviour with Diffusion Models

Diffusion models have emerged as powerful generative models in the text-...

0 Tim Pearce, et al. ∙

research

∙ 11/20/2022

UniMASK: Unified Inference in Sequential Decision Problems

Randomly masking and predicting word tokens has been a successful approa...

0 Micah Carroll, et al. ∙

research

∙ 06/20/2022

Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification

Recent years have seen a growth in user-centric applications that requir...

5 Massimiliano Patacchiola, et al. ∙

research

∙ 06/10/2022

Interactively Learning Preference Constraints in Linear Bandits

We study sequential decision-making with known rewards and unknown const...

0 David Lindner, et al. ∙

research

∙ 04/28/2022

Towards Flexible Inference in Sequential Decision Problems via Bidirectional Transformers

Randomly masking and predicting word tokens has been a successful approa...

2 Micah Carroll, et al. ∙

research

∙ 01/31/2022

Monotonic Improvement Guarantees under Non-stationarity for Decentralized PPO

We present a new monotonic improvement guarantee for optimizing decentra...

0 Mingfei Sun, et al. ∙

research

∙ 01/31/2022

You May Not Need Ratio Clipping in PPO

Proximal Policy Optimization (PPO) methods learn a policy by iteratively...

0 Mingfei Sun, et al. ∙

research

∙ 12/11/2021

Deterministic and Discriminative Imitation (D2-Imitation): Revisiting Adversarial Imitation for Sample Efficiency

Sample efficiency is crucial for imitation learning methods to be applic...

0 Mingfei Sun, et al. ∙

research

∙ 10/13/2021

NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environment

Human intelligence has the remarkable ability to adapt to new tasks and ...

0 Julia Kiseleva, et al. ∙

research

∙ 07/02/2021

Memory Efficient Meta-Learning with Large Images

Meta learning approaches to few-shot classification are computationally ...

10 John Bronskill, et al. ∙

research

∙ 07/02/2021

SocialAI: Benchmarking Socio-Cognitive Abilities in Deep Reinforcement Learning Agents

Building embodied autonomous agents capable of participating in social i...

1 Grgur Kovač, et al. ∙

research

∙ 06/16/2021

Grounding Spatio-Temporal Language with Transformers

Language is an interface to the outside world. In order for embodied age...

49 Tristan Karch, et al. ∙

research

∙ 06/06/2021

SoftDICE for Imitation Learning: Rethinking Off-policy Distribution Matching

We present SoftDICE, which achieves state-of-the-art performance for imi...

0 Mingfei Sun, et al. ∙

research

∙ 05/20/2021

Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation

A key challenge on the path to developing agents that learn complex huma...

0 Sam Devlin, et al. ∙

research

∙ 05/17/2021

Learning to Win, Lose and Cooperate through Reward Signal Evolution

Solving a reinforcement learning problem typically involves correctly pr...

0 Rafał Muszyński, et al. ∙

research

∙ 04/27/2021

SocialAI 0.1: Towards a Benchmark to Stimulate Research on Socio-Cognitive Abilities in Deep Reinforcement Learning Agents

Building embodied autonomous agents capable of participating in social i...

9 Grgur Kovač, et al. ∙

research

∙ 04/08/2021

ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition

Object recognition has made great advances in the last decade, but predo...

0 Daniela Massiceti, et al. ∙

research

∙ 03/17/2021

TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL

Training autonomous agents able to generalize to multiple tasks is a key...

51 Clément Romac, et al. ∙

research

∙ 01/14/2021

Evaluating the Robustness of Collaborative Agents

In order for agents trained by deep reinforcement learning to work along...

6 Paul Knott, et al. ∙

research

∙ 01/11/2021

Deep Interactive Bayesian Reinforcement Learning via Meta-Learning

Agents that interact with other agents often do not know a priori what t...

0 Luisa Zintgraf, et al. ∙

research

∙ 11/16/2020

Meta Automatic Curriculum Learning

A major challenge in the Deep RL (DRL) community is to train agents able...

5 Rémy Portelas, et al. ∙

research

∙ 10/02/2020

Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning

Meta-learning is a powerful tool for learning policies that can adapt ef...

4 Luisa Zintgraf, et al. ∙

research

∙ 09/01/2020

"It's Unwieldy and It Takes a Lot of Time." Challenges and Opportunities for Creating Agents in Commercial Games

Game agents such as opponents, non-player characters, and teammates are ...

15 Mikhail Jacob, et al. ∙

research

∙ 06/15/2020

Analytic Manifold Learning: Unifying and Evaluating Representations for Continuous Control

We address the problem of learning reusable state representations from s...

7 Rika Antonova, et al. ∙

research

∙ 05/12/2020

Guaranteeing Reproducibility in Deep Learning Competitions

To encourage the development of methods with reproducible and robust tra...

3 Brandon Houghton, et al. ∙

research

∙ 04/09/2020

Recognizing Spatial Configurations of Objects with Graph Neural Networks

Deep learning algorithms can be seen as compositions of functions acting...

29 Laetitia Teodorescu, et al. ∙

research

∙ 04/07/2020

Trying AGAIN instead of Trying Longer: Prior Learning for Automatic Curriculum Learning

A major challenge in the Deep RL (DRL) community is to train agents able...

1 Rémy Portelas, et al. ∙

research

∙ 03/10/2020

Automatic Curriculum Learning For Deep RL: A Short Survey

Automatic Curriculum Learning (ACL) has become a cornerstone of recent s...

30 Rémy Portelas, et al. ∙

research

∙ 10/28/2019

Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck

The ability for policies to generalize to new environments is key to the...

16 Maximilian Igl, et al. ∙

research

∙ 10/28/2019

Better Exploration with Optimistic Actor-Critic

Actor-critic methods, a type of model-free Reinforcement Learning, have ...

0 Kamil Ciosek, et al. ∙

research

∙ 10/21/2019

Variational Integrator Networks for Physically Meaningful Embeddings

Learning workable representations of dynamical systems is becoming an in...

22 Steindór Sæmundsson, et al. ∙

research

∙ 10/18/2019

VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning

Trading off exploration and exploitation in an unknown environment is ke...

66 Luisa Zintgraf, et al. ∙

research

∙ 10/16/2019

Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments

We consider the problem of how a teacher algorithm can enable an unknown...

61 Rémy Portelas, et al. ∙

research

∙ 10/07/2019

Combining No-regret and Q-learning

Counterfactual Regret Minimization (CFR) has found success in settings l...

0 Ian A. Kash, et al. ∙

research

∙ 06/04/2019

Near-Optimal Online Egalitarian learning in General Sum Repeated Matrix Games

We study two-player general sum repeated finite games where the rewards ...

0 Aristide Tossou, et al. ∙

research

∙ 04/22/2019

The MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors

Though deep reinforcement learning has led to breakthroughs in many diff...

10 William H. Guss, et al. ∙

research

∙ 01/23/2019

The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) Competition

Learning in multi-agent scenarios is a fruitful research direction, but ...

12 Diego Pérez-Liébana, et al. ∙

research

∙ 10/15/2018

Successor Uncertainties: exploration and uncertainty in temporal difference learning

We consider the problem of balancing exploration and exploitation in seq...

0 David Janz, et al. ∙

research

∙ 10/08/2018

CAML: Fast Context Adaptation via Meta-Learning

We propose CAML, a meta-learning method for fast adaptation that partiti...

2 Luisa M Zintgraf, et al. ∙

research

∙ 05/29/2018

Depth and nonlinearity induce implicit exploration for RL

The question of how to explore, i.e., take actions with uncertain outcom...

4 Justas Dauparas, et al. ∙

research

∙ 05/23/2018

Variational Inference for Data-Efficient Model Learning in POMDPs

Partially observable Markov decision processes (POMDPs) are a powerful a...

0 Sebastian Tschiatschek, et al. ∙

research

∙ 05/09/2018

Cross Domain Regularization for Neural Ranking Models Using Adversarial Learning

Unlike traditional learning to rank models that depend on hand-crafted f...

0 Daniel Cohen, et al. ∙

research

∙ 03/20/2018

Meta Reinforcement Learning with Latent Variable Gaussian Processes

Data efficiency, i.e., learning from small data sets, is critical in man...

0 Steindór Sæmundsson, et al. ∙

research

∙ 05/31/2017

The Atari Grand Challenge Dataset

Recent progress in Reinforcement Learning (RL), fueled by its combinatio...

0 Vitaly Kurin, et al. ∙

research

∙ 11/21/2016

A Deep Learning Approach for Joint Video Frame and Reward Prediction in Atari Games

Reinforcement learning is concerned with identifying reward-maximizing b...

0 Felix Leibfried, et al. ∙

research

∙ 11/21/2016

Memory Lens: How Much Memory Does an Agent Use?

We propose a new method to study the internal memory used by reinforceme...

0 Christoph Dann, et al. ∙

Katja Hofmann

Featured Co-authors