Deep learning for video game playing

In this article, we review recent Deep Learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games. We analyze the unique requirements that different game genres pose to a deep learning system and highlight important open challenges in the context of applying these machine learning methods to video games, such as general game playing, dealing with extremely large decision spaces and sparse rewards

State-based Policy Representation for Deep Policy Learning

Reinforcement Learning has achieved noticeable success in many fields, such as video game playing, continuous control, and the game of Go. One the other hand, current approaches usually require large sample complexity, and also lack the transferability to similar tasks. Imitation learning, also known as ``learning from demonstrations'', is possible to mitigate the former problem by providing successful experiences. However, current methods usually assume the expert and imitator are the same, which lack flexibility and robustness when the dynamics change.Generalizability is the core of artificial intelligence. An agent should be able to apply its knowledge for novel tasks after training in similar environments, or providing related demonstrations. Given current observation, it should have the ability to predict what can happen (modeling), and what needs to happen (planning). This brings out challenges on how to represent the knowledge and how to utilize the knowledge by learning from interactions or demonstrations.In this thesis, we will systematically study two important problems, the universal goal-reaching problem and the cross-morphology imitation learning problem, which are representative challenges in the field of reinforcement learning and imitation learning. Laying out our research work that attends to these challenging tasks unfolds our roadmap towards the holy-grail goal: make the agent generalizable by learning from observations and model the world

Dynamic Face Video Segmentation via Reinforcement Learning

For real-time semantic video segmentation, most recent works utilised a dynamic framework with a key scheduler to make online key/non-key decisions. Some works used a fixed key scheduling policy, while others proposed adaptive key scheduling methods based on heuristic strategies, both of which may lead to suboptimal global performance. To overcome this limitation, we model the online key decision process in dynamic video segmentation as a deep reinforcement learning problem and learn an efficient and effective scheduling policy from expert information about decision history and from the process of maximising global return. Moreover, we study the application of dynamic video segmentation on face videos, a field that has not been investigated before. By evaluating on the 300VW dataset, we show that the performance of our reinforcement key scheduler outperforms that of various baselines in terms of both effective key selections and running speed. Further results on the Cityscapes dataset demonstrate that our proposed method can also generalise to other scenarios. To the best of our knowledge, this is the first work to use reinforcement learning for online key-frame decision in dynamic video segmentation, and also the first work on its application on face videos.Comment: CVPR 2020. 300VW with segmentation labels is available at: https://github.com/mapleandfire/300VW-Mas

NPC AI System Based on Gameplay Recordings

Hästi optimeeritud mitte-mängija tegelased (MMT) on vastaste või meeskonna kaaslastena üheks peamiseks osaks mitme mängija mängudes. Enamus mänguroboteid on ehitatud jäikade süsteemide peal, mis võimaldavad vaid loetud arvu otsuseid ja animatsioone. Kogenud mängijad suudavad eristada mänguroboteid inimmängijatest ning ette ennustada nende liigutusi ja strateegiaid. See alandab mängukogemuse kvaliteeti. Seetõttu, eelistavad mitme mängijaga mängude mängijad mängida pigem inimmängijate kui MMTde vastu. Virtuaalreaalsuse (VR) mängud ja VR mängijad on siiani veel väike osa mängutööstusest ja mitme mängija VR mängud kannatavad mängijabaasi kaotusest, kui mänguomanikud ei suuda leida teisi mängijaid, kellega mängida. See uurimus demonstreerib mängulindistustel põhineva tehisintellekt (TI) süsteemi rakendatavust VR esimese isiku vaates tulistamismängule Vrena. Teemamäng kasutab ebatavalist liikumisesüsteemi, milles mängijad liiguvad otsiankrute abil. VR mängijate liigutuste imiteerimiseks loodi AI süsteem, mis kasutab mängulindistusi navigeerimisandmetena. Süsteem koosneb kolmest peamisest funktsionaalsusest. Need funktsionaalsused on mängutegevuse lindistamine, andmete töötlemine ja navigeerimine. Mängu keskkond on tükeldatud kuubikujulisteks sektoriteks, et vähendada erinevate asukohal põhinevate olekute arvu ning mängutegevus on lindistatud ajaintervallide ja tegevuste põhjal. Loodud mängulogid on segmenteeritud logilõikudeks ning logilõikude abil on loodud otsingutabel. Otsingutabelit kasutatakse MMT agentide navigeerimiseks ning MMTde otsuste langetamise mehanism jäljendab olek-tegevus-tasu kontseptsiooni. Loodud töövahendi kvaliteeti hinnati uuringu põhjal, millest saadi märkimisväärset tagasisidet süsteemi täiustamiseks.A well optimized Non-Player Character (NPC) as an opponent or a teammate is a major part of the multiplayer games. Most of the game bots are built upon a rigid system with numbered decisions and animations. Experienced players can distinguish bots from hu-man players and they can predict bot movements and strategies. This reduces the quality of the gameplay experience. Therefore, multiplayer game players favour playing against human players rather than NPCs. VR game market and VR gamers are still a small frac-tion of the game industry and multiplayer VR games suffer from loss of their player base if the game owners cannot find other players to play with. This study demonstrates the applicability of an Artificial Intelligence (AI) system based on gameplay recordings for a Virtual Reality (VR) First-person Shooter (FPS) game called Vrena. The subject game has an uncommon way of movement, in which the players use grappling hooks to navigate. To imitate VR players’ movements and gestures an AI system is developed which uses gameplay recordings as navigation data. The system contains three major functionality. These functionalities are gameplay recording, data refinement, and navigation. The game environment is sliced into cubic sectors to reduce the number of positional states and gameplay is recorded by time intervals and actions. Produced game logs are segmented into log sections and these log sections are used for creating a look-up table. The lookup table is used for navigating the NPC agent and the decision mechanism followed a way similar to the state-action-reward concept. The success of the developed tool is tested via a survey, which provided substantial feedback for improving the system