128 search results - page 4 / 26 » Hierarchically Optimal Average Reward Reinforcement Learning |
COLT
2007
Springer
14 years 1 months ago
2007
Springer
Bounded parameter Markov Decision Processes (BMDPs) address the issue of dealing with uncertainty in the parameters of a Markov Decision Process (MDP). Unlike the case of an MDP, t...
ICMLA
2010
13 years 5 months ago
2010
Learning the reward function of an agent by observing its behavior is termed inverse reinforcement learning and has applications in learning from demonstration or apprenticeship l...
NIPS
2001
13 years 9 months ago
2001
We consider the problem of learning to attain multiple goals in a dynamic environment, which is initially unknown. In addition, the environment may contain arbitrarily varying ele...
ICML
2005
IEEE
14 years 8 months ago
2005
IEEE
We present an efficient "sparse sampling" technique for approximating Bayes optimal decision making in reinforcement learning, addressing the well known exploration vers...
FLAIRS
2004
13 years 9 months ago
2004
er provides new techniques for abstracting the state space of a Markov Decision Process (MDP). These techniques extend one of the recent minimization models, known as -reduction, ...