202 citations · 291 across the 47 of their papers we have counts for
7 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…
Class-incremental learning: survey and performance evaluation on image classification
Marc Masana, Xialei Liu, Bartlomiej Twardowski +3
For future learning systems, incremental learning is desirable because it allows for: efficient resource usage by eliminating the need to retrain from scratch at the arrival of new…
RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning
Riccardo Del Chiaro, Bartłomiej Twardowski, Andrew D. Bagdanov +1
Research on continual learning has led to a variety of approaches to mitigating catastrophic forgetting in feed-forward classification networks. Until now surprisingly little atten…
Generative Feature Replay For Class-Incremental Learning
Xialei Liu, Chenshen Wu, Mikel Menta +5
Humans are capable of learning new tasks without forgetting previous ones, while neural networks fail due to catastrophic forgetting between new and previously-learned tasks. We co…