85 search results - page 11 / 17 » Markov Games as a Framework for Multi-Agent Reinforcement Le... |
ICML
2001
IEEE
14 years 8 months ago
2001
IEEE
Hierarchical reinforcement learning (RL) is a general framework which studies how to exploit the structure of actions and tasks to accelerate policy learning in large domains. Pri...
AAAI
2012
11 years 10 months ago
2012
Markov decision processes (MDPs) are an established framework for solving sequential decision-making problems under uncertainty. In this work, we propose a new method for batchmod...
IJCAI
2001
13 years 9 months ago
2001
This paper investigates the problem of policy learning in multiagent environments using the stochastic game framework, which we briefly overview. We introduce two properties as de...
NIPS
2007
13 years 9 months ago
2007
Bayesian Reinforcement Learning has generated substantial interest recently, as it provides an elegant solution to the exploration-exploitation trade-off in reinforcement learning...
ATAL
2010
Springer
13 years 8 months ago
2010
Springer
As learning agents move from research labs to the real world, it is increasingly important that human users, including those without programming skills, be able to teach agents de...