Online Adaptable Learning Rates for the Game Connect-4

Abstract-Learning board games by self-play has a long tradition in computational intelligence for games. Based on Tesauro's seminal success with TD-Gammon in 1994, many successful agents use temporal difference learning today. But in order to be successful with temporal difference learning on game tasks, often a careful selection of features and a large number of training games is necessary. Even for board games of moderate complexity like Connect-4, we found in previous work that a very rich initial feature set and several millions of game plays are required. In this work we investigate different approaches of online-adaptable learning rates like Incremental Delta Bar Delta (IDBD) or Temporal Coherence Learning (TCL) whether they have the potential to speed up learning for such a complex task. We propose a new variant of TCL with geometric step size changes. We compare those algorithms with several other state-of-the-art learning rate adaptation algorithms and perform a case study on the sensitivity with respect to their meta parameters. We show that in this set of learning algorithms those with geometric step size changes outperform those other algorithms with constant step size changes. Algorithms with nonlinear output functions are slightly better than linear ones. Algorithms with geometric step size changes learn faster by a factor of 4 as compared to previously published results on the task Connect-4

Decentralized Adaptive Helper Selection in Multi-channel P2P Streaming Systems

In Peer-to-Peer (P2P) multichannel live streaming, helper peers with surplus bandwidth resources act as micro-servers to compensate the server deficiencies in balancing the resources between different channel overlays. With deployment of helper level between server and peers, optimizing the user/helper topology becomes a challenging task since applying well-known reciprocity-based choking algorithms is impossible due to the one-directional nature of video streaming from helpers to users. Because of selfish behavior of peers and lack of central authority among them, selection of helpers requires coordination. In this paper, we design a distributed online helper selection mechanism which is adaptable to supply and demand pattern of various video channels. Our solution for strategic peers' exploitation from the shared resources of helpers is to guarantee the convergence to correlated equilibria (CE) among the helper selection strategies. Online convergence to the set of CE is achieved through the regret-tracking algorithm which tracks the equilibrium in the presence of stochastic dynamics of helpers' bandwidth. The resulting CE can help us select proper cooperation policies. Simulation results demonstrate that our algorithm achieves good convergence, load distribution on helpers and sustainable streaming rates for peers

Education Unleashed: Participatory Culture, Education, and Innovation in Second Life

Part of the Volume on the Ecology of Games: Connecting Youth, Games, and LearningWhile virtual worlds share common technologies and audiences with games, they possess many unique characteristics. Particularly when compared to massively multiplayer online role-playing games, virtual worlds create very different learning and teaching opportunities through markets, creation, and connections to the real world, and lack of overt game goals. This chapter aims to expose a wide audience to the breadth and depth of learning occurring within Second Life (SL). From in-world classes in the scripting language to mixed-reality conferences about the future of broadcasting, a tremendous variety of both amateurs and experts are leveraging SL as a platform for education. In one sense, this isn't new since every technology is co-opted by communities for communication, but SL is different because every aspect of it was designed to encourage this co-opting, this remixing of the virtual and the real

Language Understanding for Text-based Games Using Deep Reinforcement Learning

In this paper, we consider the task of learning control policies for text-based games. In these games, all interactions in the virtual world are through text and the underlying state is not observed. The resulting language barrier makes such environments challenging for automatic game players. We employ a deep reinforcement learning framework to jointly learn state representations and action policies using game rewards as feedback. This framework enables us to map text descriptions into vector representations that capture the semantics of the game states. We evaluate our approach on two game worlds, comparing against baselines using bag-of-words and bag-of-bigrams for state representations. Our algorithm outperforms the baselines on both worlds demonstrating the importance of learning expressive representations.Comment: 11 pages, Appearing at EMNLP, 201

Exploring Using Game-Based Learning and Gamification in a Secondary Classroom to Increase Engagement

Research has connected the importance of student engagement and student experience within the classroom but continue to use teacher directed traditional teaching methods. This project explores the use of gamification and game-based learning and how it promotes student engagement. The use of games and game-elements provide a relevant approach that focuses on student autonomy and experience, and ultimately use fun engaging ways to motivate students to learn. This project provides an entry level learning in-service opportunity for secondary educators to discover and create their own lessons that implement gamification and game-based learning in their classrooms in hopes to increase student engagement

Web-based learning and teaching resources for microscopic detection of human parasites.

DMU e-Parasitology (http://parasitology.dmu.ac.uk) presents novel web-based resources co-developed by EU academics at De Montfort University (DMU) for the teaching and learning of microscopic diagnoses of common and emerging human parasites. The package will be completed early in 2019 and presents a Virtual Laboratory and Microscope, which are equipped with engaging units for learning parasitological staining and fresh preparation techniques for detecting cysts, oocysts, eggs and spores, in conjunction with a library of digitised clinical slides. Units are equipped with short videos of academics performing the different techniques and quizzes and exercises, to provide students with the most practical experience possible

Distributed drone base station positioning for emergency cellular networks using reinforcement learning

Due to the unpredictability of natural disasters, whenever a catastrophe happens, it is vital that not only emergency rescue teams are prepared, but also that there is a functional communication network infrastructure. Hence, in order to prevent additional losses of human lives, it is crucial that network operators are able to deploy an emergency infrastructure as fast as possible. In this sense, the deployment of an intelligent, mobile, and adaptable network, through the usage of drones—unmanned aerial vehicles—is being considered as one possible alternative for emergency situations. In this paper, an intelligent solution based on reinforcement learning is proposed in order to find the best position of multiple drone small cells (DSCs) in an emergency scenario. The proposed solution’s main goal is to maximize the amount of users covered by the system, while drones are limited by both backhaul and radio access network constraints. Results show that the proposed Q-learning solution largely outperforms all other approaches with respect to all metrics considered. Hence, intelligent DSCs are considered a good alternative in order to enable the rapid and efficient deployment of an emergency communication network