18 citations · 53 across the 12 of their papers we have counts for
7 papers · 1 filter
GAN-Aimbots: Using Machine Learning for Cheating in First Person Shooters
Anssi Kanervisto, Tomi Kinnunen, Ville Hautamäki
Playing games with cheaters is not fun, and in a multi-billion-dollar video game industry with hundreds of millions of players, game developers aim to improve the security and, con…
Back to Square One: Superhuman Performance in Chutes and Ladders Through Deep Neural Networks and Tree Search
Dylan Ashley, Anssi Kanervisto, Brendan Bennett
We present AlphaChute: a state-of-the-art algorithm that achieves superhuman performance in the ancient game of Chutes and Ladders. We prove that our algorithm converges to the Nas…
General Characterization of Agents by States they Visit
Anssi Kanervisto, Tomi Kinnunen, Ville Hautamäki
Behavioural characterizations (BCs) of decision-making agents, or their policies, are used to study outcomes of training algorithms and as part of the algorithms themselves to enco…
Action Space Shaping in Deep Reinforcement Learning
Anssi Kanervisto, Christian Scheller, Ville Hautamäki
Reinforcement learning (RL) has been successful in training agents in various learning environments, including video-games. However, such work modifies and shrinks the action space…
Benchmarking End-to-End Behavioural Cloning on Video Games
Anssi Kanervisto, Joonas Pussinen, Ville Hautamäki
Behavioural cloning, where a computer is taught to perform a task based on demonstrations, has been successfully applied to various video games and robotics tasks, with and without…
Playing Minecraft with Behavioural Cloning
Anssi Kanervisto, Janne Karttunen, Ville Hautamäki
MineRL 2019 competition challenged participants to train sample-efficient agents to play Minecraft, by using a dataset of human gameplay and a limit number of steps the environment…