Topics
automata theoryBook
We discuss the problem of finding a good state repre-sentation in stochastic systems with observations. We develop a duality theory that generalizes existing work in predictive state representations as well as automata theory. We discuss how this theoretical framework can be used to build learning algorithms, approximate plan-ning algorithms as well as to deal with continuous ob-servations
In this paper, we consider {\it hybrid systems} containing both stochastic and \deterministic compon...
This paper presents an overview of the field of Stochastic Learning Automata (LA), and concentrates,...
If the state transitions of a nondeterministic or stochastic automaton are rewarded, the question ar...
We show that states of a dynamical system can be usefully represented by multi-step, action-conditio...
<p>Predictive State Representations (PSRs) are an expressive class of models for controlled stochast...
Models of agent-environment interaction that use predictive state representations (PSRs) have mainly...
In this paper, we describe how techniques from reinforcement learning might be used to approach the ...
We consider the problem of learning the behavior of a POMDP (Partially Observable Markov Decision Pr...
Learning accurate models of agent behaviours is crucial for the purpose of controlling systems where...
Models of agent-environment interaction that use predic-tive state representations (PSRs) have mainl...
The mathematics which underly the intrinsic structures of stochastic processes and dynamics of proba...
In this paper, we consider mixed systems containing both stochastic and nonstochastic components. To...
In this paper, we consider {\it hybrid systems} containing both stochastic and \deterministic compon...
In this paper, we consider {\it hybrid systems} containing both stochastic and \deterministic compon...
While various techniques exist for learning in partially observable envi-ronments such as POMDPs, th...
In this paper, we consider {\it hybrid systems} containing both stochastic and \deterministic compon...
This paper presents an overview of the field of Stochastic Learning Automata (LA), and concentrates,...
If the state transitions of a nondeterministic or stochastic automaton are rewarded, the question ar...
We show that states of a dynamical system can be usefully represented by multi-step, action-conditio...
<p>Predictive State Representations (PSRs) are an expressive class of models for controlled stochast...
Models of agent-environment interaction that use predictive state representations (PSRs) have mainly...
In this paper, we describe how techniques from reinforcement learning might be used to approach the ...
We consider the problem of learning the behavior of a POMDP (Partially Observable Markov Decision Pr...
Learning accurate models of agent behaviours is crucial for the purpose of controlling systems where...
Models of agent-environment interaction that use predic-tive state representations (PSRs) have mainl...
The mathematics which underly the intrinsic structures of stochastic processes and dynamics of proba...
In this paper, we consider mixed systems containing both stochastic and nonstochastic components. To...
In this paper, we consider {\it hybrid systems} containing both stochastic and \deterministic compon...
In this paper, we consider {\it hybrid systems} containing both stochastic and \deterministic compon...
While various techniques exist for learning in partially observable envi-ronments such as POMDPs, th...
In this paper, we consider {\it hybrid systems} containing both stochastic and \deterministic compon...
This paper presents an overview of the field of Stochastic Learning Automata (LA), and concentrates,...
If the state transitions of a nondeterministic or stochastic automaton are rewarded, the question ar...
Add to ORKG