216 citations · 233 across the 7 of their papers we have counts for
13 papers
Policy Fusion for Adaptive and Customizable Reinforcement Learning Agents
Alessandro Sestini, Alexander Kuhnle, Andrew D. Bagdanov
In this article we study the problem of training intelligent agents using Reinforcement Learning for the purpose of game development. Unlike systems built to replace human players…
Deep Policy Networks for NPC Behaviors that Adapt to Changing Design Parameters in Roguelike Games
Alessandro Sestini, Alexander Kuhnle, Andrew D. Bagdanov
Recent advances in Deep Reinforcement Learning (DRL) have largely focused on improving the performance of agents with the aim of replacing humans in known and well-defined environm…
Demonstration-efficient Inverse Reinforcement Learning in Procedurally Generated Environments
Alessandro Sestini, Alexander Kuhnle, Andrew D. Bagdanov
Deep Reinforcement Learning achieves very good results in domains where reward functions can be manually engineered. At the same time, there is growing interest within the communit…
DeepCrawl: Deep Reinforcement Learning for Turn-based Strategy Games
Alessandro Sestini, Alexander Kuhnle, Andrew D. Bagdanov
In this paper we introduce DeepCrawl, a fully-playable Roguelike prototype for iOS and Android in which all agents are controlled by policy networks trained using Deep Reinforcemen…
Process Discovery for Structured Program Synthesis
Dell Zhang, Alexander Kuhnle, Julian Richardson +1
A core task in process mining is process discovery which aims to learn an accurate process model from event log data. In this paper, we propose to use (block-) structured programs…
Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning
Hongwei Tang, Jean Rabault, Alexander Kuhnle +2
This paper focuses on the active flow control of a computational fluid dynamics simulation over a range of Reynolds numbers using deep reinforcement learning (DRL). More precisely,…