34 citations · 51 across the 2 of their papers we have counts for
5 papers
Predicting Game Engagement and Difficulty Using AI Players
Shaghayegh Roohi, Christian Guckelsberger, Asko Relas +3
This paper presents a novel approach to automated playtesting for the prediction of human player behavior and experience. It has previously been demonstrated that Deep Reinforcemen…
Predicting Game Difficulty and Churn Without Players
Shaghayegh Roohi, Asko Relas, Jari Takatalo +2
We propose a novel simulation model that is able to predict the per-level churn and pass rates of Angry Birds Dream Blast, a popular mobile free-to-play game. Our primary contribut…
Self-Imitation Learning of Locomotion Movements through Termination Curriculum
Amin Babadi, Kourosh Naderi, Perttu Hämäläinen
Animation and machine learning research have shown great advancements in the past decade, leading to robust and powerful methods for learning complex physically-based animations. H…
PPO-CMA: Proximal Policy Optimization with Covariance Matrix Adaptation
Perttu Hämäläinen, Amin Babadi, Xiaoxiao Ma +1
Proximal Policy Optimization (PPO) is a highly popular model-free reinforcement learning (RL) approach. However, we observe that in a continuous action space, PPO can prematurely s…
Intelligent Middle-Level Game Control
Amin Babadi, Kourosh Naderi, Perttu Hämäläinen
We propose the concept of intelligent middle-level game control, which lies on a continuum of control abstraction levels between the following two dual opposites: 1) high-level con…