Learning to Beat ByteRL: Exploitability of Collectible Card Game Agents

25 April 2024

Papers citing "Learning to Beat ByteRL: Exploitability of Collectible Card Game Agents"

7 / 7 papers shown

Title
Mastering Strategy Card Game (Legends of Code and Magic) via End-to-End Policy and Optimistic Smooth Fictitious Play Wei Xi Yongxin Zhang Changnan Xiao Xuefeng Huang Shihong Deng Haowei Liang Jie Chen Peng Sun OffRL 67 8 0 07 Mar 2023
Evolving Evaluation Functions for Collectible Card Game AI Radoslaw Miernik Jakub Kowalski 105 7 0 03 May 2021
Approximate exploitability: Learning a best response in large games Finbarr Timbers Nolan Bard Edward Lockhart Marc Lanctot Martin Schmid Neil Burch Julian Schrittwieser Thomas Hubert Michael Bowling AAML 73 27 0 20 Apr 2020
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures L. Espeholt Hubert Soyer Rémi Munos Karen Simonyan Volodymyr Mnih ... Vlad Firoiu Tim Harley Iain Dunning Shane Legg Koray Kavukcuoglu 247 1,607 0 05 Feb 2018
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm David Silver Thomas Hubert Julian Schrittwieser Ioannis Antonoglou Matthew Lai ... D. Kumaran T. Graepel Timothy Lillicrap Karen Simonyan Demis Hassabis 156 1,785 0 05 Dec 2017
Proximal Policy Optimization Algorithms John Schulman Filip Wolski Prafulla Dhariwal Alec Radford Oleg Klimov OffRL 568 19,296 0 20 Jul 2017
DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker Matej Moravcík Martin Schmid Neil Burch Viliam Lisý Dustin Morrill Nolan Bard Trevor Davis Kevin Waugh Michael Bradley Johanson Michael Bowling BDL 218 913 0 06 Jan 2017