32 search results - page 6 / 7 » Batch Reinforcement Learning with State Importance |
IJCNN
2008
IEEE
15 years 3 months ago
2008
IEEE
— When an agent observes its environment, there are two important characteristics of the perceived information. One is the relevance of information and the other is redundancy. T...
94
Voted
ICML
1996
IEEE
15 years 10 months ago
1996
IEEE
Some of the most successful recent applications of reinforcement learning have used neural networks and the TD algorithm to learn evaluation functions. In this paper, we examine t...
ICASSP
2008
IEEE
15 years 3 months ago
2008
IEEE
Repair or error-recovery strategies are an important design issue in Spoken Dialogue Systems (SDSs) - how to conduct the dialogue when there is no progress (e.g. due to repeated A...
ECML
2007
Springer
15 years 3 months ago
2007
Springer
We present Policy Gradient Actor-Critic (PGAC), a new model-free Reinforcement Learning (RL) method for creating limited-memory stochastic policies for Partially Observable Markov ...
AAMAS
2010
Springer
14 years 9 months ago
2010
Springer
Abstract In this paper we address the problem of simultaneous learning and coordination in multiagent Markov decision problems (MMDPs) with infinite state-spaces. We separate this ...