218 citations · 811 across the 15 of their papers we have counts for
5 papers · 1 filter
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…
LIFT: Reinforcement Learning in Computer Systems by Learning From Demonstrations
Michael Schaarschmidt, Alexander Kuhnle, Ben Ellis +3
Reinforcement learning approaches have long appealed to the data management community due to their ability to learn to control dynamic behavior from raw system performance. Recent…