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Found 7 papers, 0 papers with code
Playtesting: What is Beyond Personas
We train APF with previous paths and employ APF during the training of an RL agent.
Relational-Grid-World: A Novel Relational Reasoning Environment and An Agent Model for Relational Information Extraction
We reached similar policy optimization performance results with the PrediNet architecture and MHDPA; additionally, we achieved to extract the propositional representation explicitly ---which makes the agent's statistical policy logic more interpretable and tractable.
Using Generative Adversarial Nets on Atari Games for Feature Extraction in Deep Reinforcement Learning
In this study, Proximal Policy Optimization (PPO) algorithm is augmented with Generative Adversarial Networks (GANs) to increase the sample efficiency by enforcing the network to learn efficient representations without depending on sparse and delayed rewards as supervision.
Enhancing the Monte Carlo Tree Search Algorithm for Video Game Testing
We analyze the proposed modifications in three parts: we evaluate their effects on bug finding performances of agents, we measure their success under two different computational budgets, and we assess their effects on human-likeness of the human-like agent.
Automated Video Game Testing Using Synthetic and Human-Like Agents
We analyze the proposed method in two parts: we compare the success of human-like and synthetic agents in bug finding, and we evaluate the similarity between humanlike agents and human testers.
Multi-modal Egocentric Activity Recognition using Audio-Visual Features
In this work, we propose a new framework for egocentric activity recognition problem based on combining audio-visual features with multi-kernel learning (MKL) and multi-kernel boosting (MKBoost).
Boosted Multiple Kernel Learning for First-Person Activity Recognition
Activity recognition from first-person (ego-centric) videos has recently gained attention due to the increasing ubiquity of the wearable cameras.
