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01/19/2023
A Survey of Meta-Reinforcement Learning
While deep reinforcement learning (RL) has fueled multiple high-profile ...
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06/26/2022
Generalized Beliefs for Cooperative AI
Self-play is a common paradigm for constructing solutions in Markov game...
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12/01/2021
On the Practical Consistency of Meta-Reinforcement Learning Algorithms
Consistency is the theoretical property of a meta learning algorithm tha...
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07/17/2021
Implicit Communication as Minimum Entropy Coupling
In many common-payoff games, achieving good performance requires players...
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06/21/2021
Optimizing piano practice with a utility-based scaffold
A typical part of learning to play the piano is the progression through ...
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04/17/2021
A Self-Supervised Auxiliary Loss for Deep RL in Partially Observable Settings
In this work we explore an auxiliary loss useful for reinforcement learn...
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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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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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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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11/29/2019
VIABLE: Fast Adaptation via Backpropagating Learned Loss
In few-shot learning, typically, the loss function which is applied at t...
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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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06/06/2018
