research
∙
09/07/2023
A State Representation for Diminishing Rewards
A common setting in multitask reinforcement learning (RL) demands that a...
share
research
∙
11/26/2022
Transfer RL via the Undo Maps Formalism
Transferring knowledge across domains is one of the most fundamental pro...
research
∙
07/17/2022
Minimum Description Length Control
We propose a novel framework for multitask reinforcement learning based ...
research
∙
11/04/2021
Towards an Understanding of Default Policies in Multitask Policy Optimization
Much of the recent success of deep reinforcement learning has been drive...
research
∙
09/28/2021
A First-Occupancy Representation for Reinforcement Learning
Both animals and artificial agents benefit from state representations th...
research
∙
02/07/2021
Deep Reinforcement Learning with Dynamic Optimism
In recent years, deep off-policy actor-critic algorithms have become a d...
research
∙
10/12/2020
Efficient Wasserstein Natural Gradients for Reinforcement Learning
A novel optimization approach is proposed for application to policy grad...
research
∙
10/18/2019