5 citations · 17 across the 26 of their papers we have counts for
3 papers · 1 filter
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…