2 citations · 4 across the 6 of their papers we have counts for
5 papers · 1 filter
Successor Feature Neural Episodic Control
David Emukpere, Xavier Alameda-Pineda, Chris Reinke
A longstanding goal in reinforcement learning is to build intelligent agents that show fast learning and a flexible transfer of skills akin to humans and animals. This paper invest…
Successor Feature Representations
Chris Reinke, Xavier Alameda-Pineda
Transfer in Reinforcement Learning aims to improve learning performance on target tasks using knowledge from experienced source tasks. Successor Representations (SR) and their exte…
Progressive growing of self-organized hierarchical representations for exploration
Mayalen Etcheverry, Pierre-Yves Oudeyer, Chris Reinke
Designing agent that can autonomously discover and learn a diversity of structures and skills in unknown changing environments is key for lifelong machine learning. A central chall…
Time Adaptive Reinforcement Learning
Chris Reinke
Reinforcement learning (RL) allows to solve complex tasks such as Go often with a stronger performance than humans. However, the learned behaviors are usually fixed to specific tas…
Intrinsically Motivated Discovery of Diverse Patterns in Self-Organizing Systems
Chris Reinke, Mayalen Etcheverry, Pierre-Yves Oudeyer
In many complex dynamical systems, artificial or natural, one can observe self-organization of patterns emerging from local rules. Cellular automata, like the Game of Life (GOL), h…