11 citations · 21 across the 3 of their papers we have counts for
7 papers
Comparing Machine Learning Algorithms with or without Feature Extraction for DNA Classification
Xiangxie Zhang, Ben Beinke, Berlian Al Kindhi +1
The classification of DNA sequences is a key research area in bioinformatics as it enables researchers to conduct genomic analysis and detect possible diseases. In this paper, thre…
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…
Deep Quality-Value (DQV) Learning
Matthia Sabatelli, Gilles Louppe, Pierre Geurts +1
We introduce a novel Deep Reinforcement Learning (DRL) algorithm called Deep Quality-Value (DQV) Learning. DQV uses temporal-difference learning to train a Value neural network and…
Sampled Policy Gradient for Learning to Play the Game Agar.io
Anton Orell Wiehe, Nil Stolt Ansó, Madalina M. Drugan +1
In this paper, a new offline actor-critic learning algorithm is introduced: Sampled Policy Gradient (SPG). SPG samples in the action space to calculate an approximated policy gradi…