8 citations · 17 across the 7 of their papers we have counts for
4 papers · 1 filter
CostNet: An End-to-End Framework for Goal-Directed Reinforcement Learning
Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
Reinforcement Learning (RL) is a general framework concerned with an agent that seeks to maximize rewards in an environment. The learning typically happens through trial and error…
CaiRL: A High-Performance Reinforcement Learning Environment Toolkit
Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
This paper addresses the dire need for a platform that efficiently provides a framework for running reinforcement learning (RL) experiments. We propose the CaiRL Environment Toolki…
Interpretable Option Discovery using Deep Q-Learning and Variational Autoencoders
Per-Arne Andersen, Ole-Christoffer Granmo, Morten Goodwin
Deep Reinforcement Learning (RL) is unquestionably a robust framework to train autonomous agents in a wide variety of disciplines. However, traditional deep and shallow model-free…
The Dreaming Variational Autoencoder for Reinforcement Learning Environments
Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
Reinforcement learning has shown great potential in generalizing over raw sensory data using only a single neural network for value optimization. There are several challenges in th…