83 search results - page 14 / 17 » Planning and Acting in Partially Observable Stochastic Domai... |
NIPS
2008
14 years 9 days ago
2008
In partially observable worlds with many agents, nested beliefs are formed when agents simultaneously reason about the unknown state of the world and the beliefs of the other agen...
ECML
2007
Springer
14 years 5 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 ...
HICSS
2003
IEEE
14 years 4 months ago
2003
IEEE
This paper uses partially observable Markov decision processes (POMDP’s) as a basic framework for MultiAgent planning. We distinguish three perspectives: first one is that of a...
AAAI
2010
14 years 10 days ago
2010
This paper introduces a principled approach for the design of a scalable general reinforcement learning agent. This approach is based on a direct approximation of AIXI, a Bayesian...
ECML
2005
Springer
14 years 4 months ago
2005
Springer
Abstract. Learning to act in an unknown partially observable domain is a difficult variant of the reinforcement learning paradigm. Research in the area has focused on model-free m...