research
∙
08/08/2023
Actor-Critic with variable time discretization via sustained actions
Reinforcement learning (RL) methods work in discrete time. In order to a...
share
research
∙
03/28/2023
Reinforcement learning for optimization of energy trading strategy
An increasing part of energy is produced from renewable sources by a lar...
research
∙
11/11/2022
Emergency action termination for immediate reaction in hierarchical reinforcement learning
Hierarchical decomposition of control is unavoidable in large dynamical ...
research
∙
07/30/2022
Reinforcement learning with experience replay and adaptation of action dispersion
Effective reinforcement learning requires a proper balance of exploratio...
research
∙
01/28/2022
Graph autoencoder with constant dimensional latent space
Invertible transformation of large graphs into constant dimensional vect...
research
∙
01/17/2022
Logarithmic Continual Learning
We introduce a neural network architecture that logarithmically reduces ...
research
∙
06/23/2021
Multiband VAE: Latent Space Partitioning for Knowledge Consolidation in Continual Learning
We propose a new method for unsupervised continual knowledge consolidati...
research
∙
05/28/2021
Reinforcement Learning for on-line Sequence Transformation
A number of problems in the processing of sound and natural language, as...
research
∙
05/28/2021
Deep Memory Update
Recurrent neural networks are key tools for sequential data processing. ...
research
∙
04/08/2021
ACERAC: Efficient reinforcement learning in fine time discretization
We propose a framework for reinforcement learning (RL) in fine time disc...
research
∙
09/10/2020
A framework for reinforcement learning with autocorrelated actions
The subject of this paper is reinforcement learning. Policies are consid...
research
∙
01/23/2020