Jan 23, 2019 · In this paper, we introduce a geometric framework for formulating agent objectives in zero-sum games, in order to construct adaptive sequences ...
This paper proposes algorithms that adaptively and continually pose new, useful objectives which result in open- ended learning in two-player zero-sum games.
A geometric framework for formulating agent objectives in zero-sum games is introduced, and a new algorithm (rectified Nash response, PSRO_rN) is developed ...
Abstract. Zero-sum games such as chess and poker are, ab- stractly, functions that evaluate pairs of agents, for example labeling them 'winner' and 'loser'.
Depending on the version of the story, the first boy was either. Lord Kelvin or James Clerk Maxwell. The second boy indeed.
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Zero-sum games such as chess and poker are, abstractly, functions that evaluate pairs of agents, for example labeling them 'winner' and 'loser'.
Jan 25, 2019 · 43K subscribers in the reinforcementlearning community. Reinforcement learning is a subfield of AI/statistics focused on ...
Open-ended learning in symmetric zero-sum games. D. Balduzzi, M. Garnelo, Y. Bachrach, W. Czarnecki, J. Pérolat, M. Jaderberg, and T. Graepel.
May 29, 2019 · It shows how to build useful objectives in non-transitive games, where there isn't necessarily a clear winner–such as rock-paper-scissors, ...
In this work, we summarize previous concepts of diversity and work towards offering a unified measure of diversity in multi-agent open-ended learning to include ...