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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...
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05/25/2023
Learning Safety Constraints from Demonstrations with Unknown Rewards
We propose Convex Constraint Learning for Reinforcement Learning (CoCoRL...
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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...
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02/15/2023
Trust-Region-Free Policy Optimization for Stochastic Policies
Trust Region Policy Optimization (TRPO) is an iterative method that simu...
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01/25/2023
Imitating Human Behaviour with Diffusion Models
Diffusion models have emerged as powerful generative models in the text-...
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11/20/2022
UniMASK: Unified Inference in Sequential Decision Problems
Randomly masking and predicting word tokens has been a successful approa...
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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...
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06/10/2022
Interactively Learning Preference Constraints in Linear Bandits
We study sequential decision-making with known rewards and unknown const...
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04/28/2022
Towards Flexible Inference in Sequential Decision Problems via Bidirectional Transformers
Randomly masking and predicting word tokens has been a successful approa...
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01/31/2022
Monotonic Improvement Guarantees under Non-stationarity for Decentralized PPO
We present a new monotonic improvement guarantee for optimizing decentra...
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01/31/2022
You May Not Need Ratio Clipping in PPO
Proximal Policy Optimization (PPO) methods learn a policy by iteratively...
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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...
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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 ...
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07/02/2021
Memory Efficient Meta-Learning with Large Images
Meta learning approaches to few-shot classification are computationally ...
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07/02/2021
SocialAI: Benchmarking Socio-Cognitive Abilities in Deep Reinforcement Learning Agents
Building embodied autonomous agents capable of participating in social i...
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06/16/2021
Grounding Spatio-Temporal Language with Transformers
Language is an interface to the outside world. In order for embodied age...
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06/06/2021
SoftDICE for Imitation Learning: Rethinking Off-policy Distribution Matching
We present SoftDICE, which achieves state-of-the-art performance for imi...
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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...
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05/17/2021
Learning to Win, Lose and Cooperate through Reward Signal Evolution
Solving a reinforcement learning problem typically involves correctly pr...
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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...
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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...
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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...
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01/14/2021
Evaluating the Robustness of Collaborative Agents
In order for agents trained by deep reinforcement learning to work along...
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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...
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11/16/2020
Meta Automatic Curriculum Learning
A major challenge in the Deep RL (DRL) community is to train agents able...
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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...
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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 ...
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06/15/2020
Analytic Manifold Learning: Unifying and Evaluating Representations for Continuous Control
We address the problem of learning reusable state representations from s...
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05/12/2020
Guaranteeing Reproducibility in Deep Learning Competitions
To encourage the development of methods with reproducible and robust tra...
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04/09/2020
Recognizing Spatial Configurations of Objects with Graph Neural Networks
Deep learning algorithms can be seen as compositions of functions acting...
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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...
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03/10/2020
Automatic Curriculum Learning For Deep RL: A Short Survey
Automatic Curriculum Learning (ACL) has become a cornerstone of recent s...
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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...
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10/28/2019
Better Exploration with Optimistic Actor-Critic
Actor-critic methods, a type of model-free Reinforcement Learning, have ...
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10/21/2019
Variational Integrator Networks for Physically Meaningful Embeddings
Learning workable representations of dynamical systems is becoming an in...
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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...
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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...
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10/07/2019
Combining No-regret and Q-learning
Counterfactual Regret Minimization (CFR) has found success in settings l...
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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 ...
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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...
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01/23/2019
The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) Competition
Learning in multi-agent scenarios is a fruitful research direction, but ...
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10/15/2018
Successor Uncertainties: exploration and uncertainty in temporal difference learning
We consider the problem of balancing exploration and exploitation in seq...
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10/08/2018
CAML: Fast Context Adaptation via Meta-Learning
We propose CAML, a meta-learning method for fast adaptation that partiti...
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05/29/2018
Depth and nonlinearity induce implicit exploration for RL
The question of how to explore, i.e., take actions with uncertain outcom...
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05/23/2018
Variational Inference for Data-Efficient Model Learning in POMDPs
Partially observable Markov decision processes (POMDPs) are a powerful a...
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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...
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03/20/2018
Meta Reinforcement Learning with Latent Variable Gaussian Processes
Data efficiency, i.e., learning from small data sets, is critical in man...
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05/31/2017
The Atari Grand Challenge Dataset
Recent progress in Reinforcement Learning (RL), fueled by its combinatio...
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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...
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11/21/2016
