most citedContinual World: A Robotic Benchmark For Continual Reinforcement Learning

20 citations · 31 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2021★ 1 cited

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…

cs.LG2021★ 4 cited

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…

cs.AI2021

Subgoal Search For Complex Reasoning Tasks

Konrad Czechowski, Tomasz Odrzygóźdź, Marek Zbysiński +5

Humans excel in solving complex reasoning tasks through a mental process of moving from one idea to a related one. Inspired by this, we propose Subgoal Search (kSubS) method. Its k…

cs.LG2021★ 20 cited

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…

cs.LG2019★ 6 cited

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…