72 citations · 172 across the 18 of their papers we have counts for
16 papers · 1 filter
A Bayesian Solution To The Imitation Gap
Risto Vuorio, Mattie Fellows, Cong Lu +2
In many real-world settings, an agent must learn to act in environments where no reward signal can be specified, but a set of expert demonstrations is available. Imitation learning…
SplAgger: Split Aggregation for Meta-Reinforcement Learning
Jacob Beck, Matthew Jackson, Risto Vuorio +2
A core ambition of reinforcement learning (RL) is the creation of agents capable of rapid learning in novel tasks. Meta-RL aims to achieve this by directly learning such agents. Bl…
Distilling Morphology-Conditioned Hypernetworks for Efficient Universal Morphology Control
Zheng Xiong, Risto Vuorio, Jacob Beck +3
Learning a universal policy across different robot morphologies can significantly improve learning efficiency and enable zero-shot generalization to unseen morphologies. However, l…
Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design
Matthew Thomas Jackson, Minqi Jiang, Jack Parker-Holder +5
The past decade has seen vast progress in deep reinforcement learning (RL) on the back of algorithms manually designed by human researchers. Recently, it has been shown that it is…
Recurrent Hypernetworks are Surprisingly Strong in Meta-RL
Jacob Beck, Risto Vuorio, Zheng Xiong +1
Deep reinforcement learning (RL) is notoriously impractical to deploy due to sample inefficiency. Meta-RL directly addresses this sample inefficiency by learning to perform few-sho…
A Tutorial on Meta-Reinforcement Learning
Jacob Beck, Risto Vuorio, Evan Zheran Liu +4
While deep reinforcement learning (RL) has fueled multiple high-profile successes in machine learning, it is held back from more widespread adoption by its often poor data efficien…
Deconfounding Imitation Learning with Variational Inference
Risto Vuorio, Pim de Haan, Johann Brehmer +3
Standard imitation learning can fail when the expert demonstrators have different sensory inputs than the imitating agent. This is because partial observability gives rise to hidde…
Hypernetworks in Meta-Reinforcement Learning
Jacob Beck, Matthew Thomas Jackson, Risto Vuorio +1
Training a reinforcement learning (RL) agent on a real-world robotics task remains generally impractical due to sample inefficiency. Multi-task RL and meta-RL aim to improve sample…
An Investigation of the Bias-Variance Tradeoff in Meta-Gradients
Risto Vuorio, Jacob Beck, Shimon Whiteson +2
Meta-gradients provide a general approach for optimizing the meta-parameters of reinforcement learning (RL) algorithms. Estimation of meta-gradients is central to the performance o…
On the Practical Consistency of Meta-Reinforcement Learning Algorithms
Zheng Xiong, Luisa Zintgraf, Jacob Beck +2
Consistency is the theoretical property of a meta learning algorithm that ensures that, under certain assumptions, it can adapt to any task at test time. An open question is whethe…
Adaptive Pairwise Weights for Temporal Credit Assignment
Zeyu Zheng, Risto Vuorio, Richard Lewis +1
How much credit (or blame) should an action taken in a state get for a future reward? This is the fundamental temporal credit assignment problem in Reinforcement Learning (RL). One…
Learning State Representations from Random Deep Action-conditional Predictions
Zeyu Zheng, Vivek Veeriah, Risto Vuorio +2
Our main contribution in this work is an empirical finding that random General Value Functions (GVFs), i.e., deep action-conditional predictions -- random both in what feature of o…
Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem
John Holler, Risto Vuorio, Zhiwei Qin +6
Order dispatching and driver repositioning (also known as fleet management) in the face of spatially and temporally varying supply and demand are central to a ride-sharing platform…
Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation
Risto Vuorio, Shao-Hua Sun, Hexiang Hu +1
Model-agnostic meta-learners aim to acquire meta-learned parameters from similar tasks to adapt to novel tasks from the same distribution with few gradient updates. With the flexib…
Toward Multimodal Model-Agnostic Meta-Learning
Risto Vuorio, Shao-Hua Sun, Hexiang Hu +1
Gradient-based meta-learners such as MAML are able to learn a meta-prior from similar tasks to adapt to novel tasks from the same distribution with few gradient updates. One import…
Meta Continual Learning
Risto Vuorio, Dong-Yeon Cho, Daejoong Kim +1
Using neural networks in practical settings would benefit from the ability of the networks to learn new tasks throughout their lifetimes without forgetting the previous tasks. This…