20 citations · 31 across the 5 of their papers we have counts for
4 papers · 1 filter
Continuous Control With Ensemble Deep Deterministic Policy Gradients
Piotr Januszewski, Mateusz Olko, Michał Królikowski +4
The growth of deep reinforcement learning (RL) has brought multiple exciting tools and methods to the field. This rapid expansion makes it important to understand the interplay bet…
Catalytic Role Of Noise And Necessity Of Inductive Biases In The Emergence Of Compositional Communication
Łukasz Kuciński, Tomasz Korbak, Paweł Kołodziej +1
Communication is compositional if complex signals can be represented as a combination of simpler subparts. In this paper, we theoretically show that inductive biases on both the tr…
Continual World: A Robotic Benchmark For Continual Reinforcement Learning
Maciej Wołczyk, Michał Zając, Razvan Pascanu +2
Continual learning (CL) -- the ability to continuously learn, building on previously acquired knowledge -- is a natural requirement for long-lived autonomous reinforcement learning…
Uncertainty-sensitive Learning and Planning with Ensembles
Piotr Miłoś, Łukasz Kuciński, Konrad Czechowski +2
We propose a reinforcement learning framework for discrete environments in which an agent makes both strategic and tactical decisions. The former manifests itself through the use o…