Automating gameplay in a complex environment poses challenges for learning due to the large state and action spaces and the need for long-term planning.This paper presents a Gymnasium environment to allow for reinforcement learning research and experimentation in the video game RollerCoaster Tycoon, a popular amusement park simulation game with complex mechanics.We also present an approach to learn to play and win several game scenarios in this environment.

The “RollerCoaster Tycoon” video game involves creating rollercoaster tracks that optimize for various game metrics while also being constrained by the need to ensure a feasible structure in terms of physical and spatial bounds. Creating these procedurally is thus a challenge. In this work, we explore multiple approaches to rollercoaster track generation through the use of Markov chains and various deep learning methods. We show that we can achieve relatively good tracks in terms of the game's measurement of success, and that reinforcement learning allows for more control of the generated tracks and for different rider experiences. A focus on multiple measures allows our work to extend to other track properties drawn from real-world research. This paper extends a previous publication by adding a new reward function for our reinforcement learning agent as well as further analyses of the generated tracks, including a metric measuring rider excitement over time, a revised novelty metric and an analysis of controllability.

The "RollerCoaster Tycoon" video game involves creating rollercoasters that optimize for various in-game metrics, while also being constrained by the need to ensure a feasible structure in terms of physical and spatial bounds. Creating these procedurally is thus a challenge. In this work, we explore multiple approaches to rollercoaster generation, including Markov chains and machine learning and reinforcement learning algorithms. We show that we can achieve relatively good tracks in terms of the game’s measurement of success, and that reinforcement learning may give more control over other factors of potential interest. A focus on multiple measures allows our work to extend to other factors that also mimic actual player constructions.

Only a small percentage of blind and low-vision people use traditional mobility aids such as a cane or a guide dog. Various assistive technologies have been proposed to address the limitations of traditional mobility aids. These devices often give either the user or the device majority of the control. In this work, we explore how varying levels of control affect the users' sense of agency, trust in the device, confidence, and successful navigation. We present Glide, a novel mobility aid with two modes for control: Glide-directed and User-directed. We employ Glide in a study (N=9) in which blind or low-vision participants used both modes to navigate through an indoor environment. Overall, participants found that Glide was easy to use and learn. Most participants trusted Glide despite its current limitations, and their confidence and performance increased as they continued to use Glide. Users' control mode preferences varied in different situations; no single mode "won" in all situations.

Roguelike games generally feature exploration problems as a critical yet often repetitive element of gameplay. Automated approaches, however, face challenges in terms of optimality, as well as due to incomplete information, such as from the presence of secret doors. This paper presents an algorithmic approach to exploration of roguelike dungeon environments. Our design aims to minimize exploration time, balancing coverage and discovery of secret areas with resource cost. Our algorithm is based on the concept of occupancy maps popular in robotics, adapted to encourage efficient discovery of secret access points. Through extensive experimentation on NetHack maps, we show that this technique is significantly more efficient than simpler greedy approaches and an existing automated player. We further investigate optimized parameterization for the algorithm through a comprehensive data analysis. These results point toward better automation for players, as well as heuristics applicable to fully automated gameplay.

Roguelike games generally feature exploration problems as a critical, yet often repetitive element of gameplay. Automated approaches, however, face challenges in terms of optimality. This paper presents an approach to exploration of roguelike dungeon environments. Our design, based on the concept of occupancy maps popular in robotics, aims to minimize exploration time, balancing coverage with resource cost. Through extensive experimentation on NetHack maps we show that this technique is significantly more efficient than simpler greedy approaches. Results point towards better automation for players as well as heuristics for fully automated gameplay.

Bluetooth is a promising wireless technology that enables devices to form short-range multihop wireless ad-hoc networks, or personal area networks (PANs). However, scatternet formation is one of the challenges that need to be resolved since the performance of a Bluetooth network depends largely on the scatternet topology used. We first identify a particular variant of a height-balanced binary tree, termed ACB-tree for almost-complete-binary tree, that allows two such trees to be combined to create a larger ACB-tree while retaining the height-balance requirement. We then present a distributed scatternet formation algorithm for creation of ACB-trees. We further extend the algorithm to produce an ACB-tree scatternet with energy efficient properties. We also present simulations, conducted using Blueware simulator, to provide experiment results to study and compare the performance of the resulting scatternets.