1 citations · 1 across the 4 of their papers we have counts for
6 papers
Coachable agents for interactive gameplay
Roberto Capobianco, Harm van Seijen, Nolan D. Bard +38
Reinforcement learning has proven to be a valuable tool in the creation of advanced AI and robotic systems, contributing to everything from game playing to robotics to foundation m…
ETHER: Aligning Emergent Communication for Hindsight Experience Replay
Kevin Denamganaï, Kevin Yandoka Denamganaï, Daniel Hernandez +3
Hindsight Experience Replay (HER) enhances sample efficiency in goal-conditioned reinforcement learning (RL) by relabelling failed trajectories with goals that were actually achiev…
BRExIt: On Opponent Modelling in Expert Iteration
Daniel Hernandez, Hendrik Baier, Michael Kaisers
Finding a best response policy is a central objective in game theory and multi-agent learning, with modern population-based training approaches employing reinforcement learning alg…
A Comparison of Self-Play Algorithms Under a Generalized Framework
Daniel Hernandez, Kevin Denamganai, Sam Devlin +2
Throughout scientific history, overarching theoretical frameworks have allowed researchers to grow beyond personal intuitions and culturally biased theories. They allow to verify a…
Metagame Autobalancing for Competitive Multiplayer Games
Daniel Hernandez, Charles Takashi Toyin Gbadamosi, James Goodman +1
Automated game balancing has often focused on single-agent scenarios. In this paper we present a tool for balancing multi-player games during game design. Our approach requires a d…
Learning Socially Appropriate Robot Approaching Behavior Toward Groups using Deep Reinforcement Learning
Yuan Gao, Fangkai Yang, Martin Frisk +3
Deep reinforcement learning has recently been widely applied in robotics to study tasks such as locomotion and grasping, but its application to social human-robot interaction (HRI)…