56 search results - page 7 / 12 » Reinforcement Learning for Average Reward Zero-Sum Games |
NIPS
2001
13 years 9 months ago
2001
Policy gradient methods for reinforcement learning avoid some of the undesirable properties of the value function approaches, such as policy degradation (Baxter and Bartlett, 2001...
AAMAS
2007
Springer
14 years 1 months ago
2007
Springer
Learning Automata (LA) were recently shown to be valuable tools for designing Multi-Agent Reinforcement Learning algorithms. One of the principal contributions of LA theory is that...
ATAL
2007
Springer
14 years 1 months ago
2007
Springer
This paper proposes the β-WoLF algorithm for multiagent reinforcement learning (MARL) in the stochastic games framework that uses an additional “advice” signal to inform agen...
CORR
2007
Springer
13 years 7 months ago
2007
Springer
—We consider an agent interacting with an unmodeled environment. At each time, the agent makes an observation, takes an action, and incurs a cost. Its actions can influence futu...
ICML
2005
IEEE
14 years 8 months ago
2005
IEEE
Learning algorithms often obtain relatively low average payoffs in repeated general-sum games between other learning agents due to a focus on myopic best-response and one-shot Nas...