Self-Playing Labyrinth Game Using Camera and Industrial Control System

In this master’s thesis, an industrial control system together with a network camera and servo motors were used to automate a ball and plate labyrinth system. The two servo motors, each with its own servo drive, were connected by joint arms to the plate resting on two interconnected gimbal frames, one for each axis. A background subtraction-based ball position tracking algorithm was developed to measure the ball-position using the camera. The camera acted as a sensor node in a control network with a programmable logical controller used together with the servo drives to implement a cascaded PID control loop to control the ball position. The ball reference position could either be controlled with user input from a tablet device, or automatically to make the labyrinth self-playing. The resulting system was able to control the ball position through the labyrinth using the camera for position feedback

ProsocialLearn: D2.3 - 1st system requirements and architecture

This document present the first version of the ProsocialLearn architecture covering the principle definition, the requirement collection, the “business”, “information system”, “technology” architecture as defined in the TOGAF methodology

The Baroque in Games: A Case Study of Remediation

This thesis presents a case study for the remediation of baroque painting by some contemporary video games. Video games such as Horizon Zero Dawn borrow illusionistic techniques, the motif of the labyrinth, and the use of the total work of art as presented in certain baroque paintings. These characteristics are modified and represented within the new medium in an effort to heighten immediacy and create an immersive experience for the audience. This thesis discusses the process of remediation in detail and then analyzes how video games—and Horizon Zero Dawn in particular—remediate illusionistic techniques like linear and atmospheric perspective. Then, the analysis focuses on the remediation of the baroque labyrinth. Finally, the thesis analyzes the total work of art present in this case study of baroque painting and video games and how the former media improved upon the latter. Within the context of this case study, this thesis finds that certain video games borrow illusionistic techniques, the labyrinth motif, and the total work of art from baroque painting and repurpose these aspects in the new medium

Learning Curricula in Open-Ended Worlds

Deep reinforcement learning (RL) provides powerful methods for training optimal sequential decision-making agents. As collecting real-world interactions can entail additional costs and safety risks, the common paradigm of sim2real conducts training in a simulator, followed by real-world deployment. Unfortunately, RL agents easily overfit to the choice of simulated training environments, and worse still, learning ends when the agent masters the specific set of simulated environments. In contrast, the real world is highly open-ended, featuring endlessly evolving environments and challenges, making such RL approaches unsuitable. Simply randomizing over simulated environments is insufficient, as it requires making arbitrary distributional assumptions and can be combinatorially less likely to sample specific environment instances that are useful for learning. An ideal learning process should automatically adapt the training environment to maximize the learning potential of the agent over an open-ended task space that matches or surpasses the complexity of the real world. This thesis develops a class of methods called Unsupervised Environment Design (UED), which aim to produce such open-ended processes. Given an environment design space, UED automatically generates an infinite sequence or curriculum of training environments at the frontier of the learning agent's capabilities. Through extensive empirical studies and theoretical arguments founded on minimax-regret decision theory and game theory, the findings in this thesis show that UED autocurricula can produce RL agents exhibiting significantly improved robustness and generalization to previously unseen environment instances. Such autocurricula are promising paths toward open-ended learning systems that achieve more general intelligence by continually generating and mastering additional challenges of their own design.Comment: PhD dissertatio

Learning Curricula in Open-Ended Worlds

Deep reinforcement learning (RL) provides powerful methods for training optimal sequential decision-making agents. As collecting real-world interactions can entail additional costs and safety risks, the common paradigm of sim2real conducts training in a simulator, followed by real-world deployment. Unfortunately, RL agents easily overfit to the choice of simulated training environments, and worse still, learning ends when the agent masters the specific set of simulated environments. In contrast, the real-world is highly open-ended—featuring endlessly evolving environments and challenges, making such RL approaches unsuitable. Simply randomizing across a large space of simulated environments is insufficient, as it requires making arbitrary distributional assumptions, and as the design space grows, it can become combinatorially less likely to sample specific environment instances that are useful for learning. An ideal learning process should automatically adapt the training environment to maximize the learning potential of the agent over an open-ended task space that matches or surpasses the complexity of the real world. This thesis develops a class of methods called Unsupervised Environment Design (UED), which seeks to enable such an open-ended process via a principled approach for gradually improving the robustness and generality of the learning agent. Given a potentially open-ended environment design space, UED automatically generates an infinite sequence or curriculum of training environments at the frontier of the learning agent’s capabilities. Through both extensive empirical studies and theoretical arguments founded on minimax-regret decision theory and game theory, the findings in this thesis show that UED autocurricula can produce RL agents exhibiting significantly improved robustness and generalization to previously unseen environment instances. Such autocurricula are promising paths toward open-ended learning systems that approach general intelligence—a long sought-after ambition of artificial intelligence research—by continually generating and mastering additional challenges of their own design