Search Results for author: Alessandro Sestini

Found 11 papers, 0 papers with code

Improving Generalization in Game Agents with Data Augmentation in Imitation Learning

no code implementations22 Sep 2023 Derek Yadgaroff, Alessandro Sestini, Konrad Tollmar, Linus Gisslén

Imitation learning is an effective approach for training game-playing agents and, consequently, for efficient game production.

Data Augmentation Imitation Learning

Generating Personas for Games with Multimodal Adversarial Imitation Learning

no code implementations15 Aug 2023 William Ahlberg, Alessandro Sestini, Konrad Tollmar, Linus Gisslén

MultiGAIL is based on generative adversarial imitation learning and uses multiple discriminators as reward models, inferring the environment reward by comparing the agent and distinct expert policies.

Imitation Learning reinforcement-learning

Efficient Ground Vehicle Path Following in Game AI

no code implementations7 Jul 2023 Rodrigue de Schaetzen, Alessandro Sestini

This short paper presents an efficient path following solution for ground vehicles tailored to game AI.

Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learning

no code implementations15 Aug 2022 Alessandro Sestini, Joakim Bergdahl, Konrad Tollmar, Andrew D. Bagdanov, Linus Gisslén

In games, as in and many other domains, design validation and testing is a huge challenge as systems are growing in size and manual testing is becoming infeasible.

Imitation Learning valid

Contextual Decision Trees

no code implementations13 Jul 2022 Tommaso Aldinucci, Enrico Civitelli, Leonardo di Gangi, Alessandro Sestini

Focusing on Random Forests, we propose a multi-armed contextual bandit recommendation framework for feature-based selection of a single shallow tree of the learned ensemble.

CCPT: Automatic Gameplay Testing and Validation with Curiosity-Conditioned Proximal Trajectories

no code implementations21 Feb 2022 Alessandro Sestini, Linus Gisslén, Joakim Bergdahl, Konrad Tollmar, Andrew D. Bagdanov

This paper proposes a novel deep reinforcement learning algorithm to perform automatic analysis and detection of gameplay issues in complex 3D navigation environments.

Imitation Learning reinforcement-learning +1

Deep Policy Networks for NPC Behaviors that Adapt to Changing Design Parameters in Roguelike Games

no code implementations7 Dec 2020 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 environments.

Demonstration-efficient Inverse Reinforcement Learning in Procedurally Generated Environments

no code implementations4 Dec 2020 Alessandro Sestini, Alexander Kuhnle, Andrew D. Bagdanov

We propose a technique based on Adversarial Inverse Reinforcement Learning which can significantly decrease the need for expert demonstrations in PCG games.

reinforcement-learning Reinforcement Learning (RL)

DeepCrawl: Deep Reinforcement Learning for Turn-based Strategy Games

no code implementations3 Dec 2020 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 Reinforcement Learning (DRL).

reinforcement-learning Reinforcement Learning (RL)

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