As applications for artificially intelligent agents increase in complexity we can no longer rely on clever heuristics and hand-tuned behaviors to develop their programming. Even t...
Shawn Arseneau, Wei Sun, Changpeng Zhao, Jeremy R....
Reinforcement Learning (RL) is the study of programs that improve their performance by receiving rewards and punishments from the environment. Most RL methods optimize the discoun...
In this paper we introduce Challenger, a multiagent system that performs completely distributed resource allocation. Challenger consists of agents which individually manage local ...
It is well-known that acting in an individually rational manner, according to the principles of classical game theory, may lead to sub-optimal solutions in a class of problems nam...
The field of multiagent decision making is extending its tools from classical game theory by embracing reinforcement learning, statistical analysis, and opponent modeling. For ex...
Michael Wunder, Michael Kaisers, John Robert Yaros...