In recent years, reinforcement learning has been successful in solving video games from Atari to Star Craft II. However, the end-to-end model-free reinforcement learning (RL) is not sample efficient and requires a significant amount of computational resources to achieve superhuman level performance. Model-free RL is also unlikely to produce human-like agents for playtesting and gameplaying AI in the development cycle of complex video games. In this paper, we present a hierarchical approach to training agents with the goal of achieving human-like style and high skill level in team sports games. While this is still work in progress, our preliminary results show that the presented approach holds promise for solving the posed multi-agent learning problem.
Objective:The current status of human–robot interaction (HRI) is reviewed, and key current research challenges for the human factors community are described.Background:Robots have evolved from continuous human-controlled master–slave servomechanisms for handling nuclear waste to a broad range of robots incorporating artificial intelligence for many applications and under human supervisory control.Methods:This mini-review describes HRI developments in four application areas and what are the challenges for human factors research.Results:In addition to a plethora of research papers, evidence of success is manifest in live demonstrations of robot capability under various forms of human control.Conclusions:HRI is a rapidly evolving field. Specialized robots under human teleoperation have proven successful in hazardous environments and medical application, as have specialized telerobots under human supervisory control for space and repetitive industrial tasks. Research in areas of self-driving cars, intimate co... This study presents a learning-by-imitation technique that learns social robot interaction behaviors from natural human- human interaction data and requires minimum input from a designer. To solve the problem of responding to ambiguous human actions, a novel topic clustering algorithm based on action cooccurrence frequencies is introduced. The system learns human-readable rules that dictate which action the robot should take, based on the most recent human action and the current estimated topic of conversation. The technique is demonstrated in a scenario where the robot learns to play the role of a travel agent. The proposed technique outperformed several baseline techniques in qualitative and quantitative evaluations. It responded more accurately to ambiguous questions and participants found it was easier to understand, provided more information, and required less effort to interact with.