22 search results - page 1 / 5 » Reinforcement Learning for Nash Equilibrium Generation |
ATAL
2009
Springer
13 years 11 months ago
2009
Springer
This paper introduces a multiagent reinforcement learning algorithm that converges with a given accuracy to stationary Nash equilibria in general-sum discounted stochastic games. ...
ICML
1998
IEEE
14 years 5 months ago
1998
IEEE
In this paper, we adopt general-sum stochastic games as a framework for multiagent reinforcement learning. Our work extends previous work by Littman on zero-sum stochastic games t...
ANOR
2005
13 years 4 months ago
2005
The main objects here are finite-strategy games in which entropic terms are subtracted from the payoffs. After such subtraction each Nash equilibrium solves an explicit, unconstra...
ECML
2003
Springer
13 years 10 months ago
2003
Springer
Abstract. Most of multi-agent reinforcement learning algorithms aim to converge to a Nash equilibrium, but a Nash equilibrium does not necessarily mean a desirable result. On the o...
ATAL
2009
Springer
13 years 11 months ago
2009
Springer
We present a general methodology to automate the search for equilibrium strategies in games derived from computational experimentation. Our approach interleaves empirical game-the...