11 citations · 23 across the 5 of their papers we have counts for
5 papers · 1 filter
Fractional Transfer Learning for Deep Model-Based Reinforcement Learning
Remo Sasso, Matthia Sabatelli, Marco A. Wiering
Reinforcement learning (RL) is well known for requiring large amounts of data in order for RL agents to learn to perform complex tasks. Recent progress in model-based RL allows age…
Enhancing reinforcement learning by a finite reward response filter with a case study in intelligent structural control
Hamid Radmard Rahmani, Carsten Koenke, Marco A. Wiering
In many reinforcement learning (RL) problems, it takes some time until a taken action by the agent reaches its maximum effect on the environment and consequently the agent receives…
Continuous-action Reinforcement Learning for Playing Racing Games: Comparing SPG to PPO
Mario S. Holubar, Marco A. Wiering
In this paper, a novel racing environment for OpenAI Gym is introduced. This environment operates with continuous action- and state-spaces and requires agents to learn to control t…
Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms
Matthia Sabatelli, Gilles Louppe, Pierre Geurts +1
This paper makes one step forward towards characterizing a new family of \textit{model-free} Deep Reinforcement Learning (DRL) algorithms. The aim of these algorithms is to jointly…
Comparing Generative Adversarial Network Techniques for Image Creation and Modification
Mathijs Pieters, Marco Wiering
Generative adversarial networks (GANs) have demonstrated to be successful at generating realistic real-world images. In this paper we compare various GAN techniques, both supervise…