In this chapter, we introduce the notion of simple context-free decision processes, which are an extension of episodic finite-state Markov decision processes (MDPs). Intuitively, a simple context-free decision process can be thought of as an episodic finite-state MDP with a stack. In fact, many reinforcement learning methods can be applied to the class of simpl
This paper provides new techniques for abstracting the state space of a Markov Decision Process (MD...
Learning, planning, and representing knowledge at multiple levels of temporal abstraction are key, l...
We study reinforcement learning for continuous-time Markov decision processes (MDPs) in the finite-h...
General purpose intelligent learning agents cycle through (complex,non-MDP) sequences of observation...
This paper presents a simple method for exact online inference and approximate decision making, appl...
General purpose intelligent learning agents cycle through (complex,non-MDP) sequences of observation...
Reinforcement learning in non-stationary environments is generally regarded as a very difficult prob...
Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision probl...
Learning and reasoning in large, structured, probabilistic worlds is at the heart of artificial inte...
To operate effectively in complex environments learning agents require the ability to form useful ab...
We introduce and study Regular Decision Processes (RDPs), a new, compact, factored model for domains...
Reinforcement learning (RL) algorithms provide a sound theoretical basis for building learning contr...
Markov decision processes (MDPs) have long been used to model qualitative aspects of systems in the ...
We present a class of metrics, defined on the state space of a finite Markov decision process (MDP)...
Learning, planning, and representing knowledge at multiple levels of temporal abstraction are key, l...
This paper provides new techniques for abstracting the state space of a Markov Decision Process (MD...
Learning, planning, and representing knowledge at multiple levels of temporal abstraction are key, l...
We study reinforcement learning for continuous-time Markov decision processes (MDPs) in the finite-h...
General purpose intelligent learning agents cycle through (complex,non-MDP) sequences of observation...
This paper presents a simple method for exact online inference and approximate decision making, appl...
General purpose intelligent learning agents cycle through (complex,non-MDP) sequences of observation...
Reinforcement learning in non-stationary environments is generally regarded as a very difficult prob...
Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision probl...
Learning and reasoning in large, structured, probabilistic worlds is at the heart of artificial inte...
To operate effectively in complex environments learning agents require the ability to form useful ab...
We introduce and study Regular Decision Processes (RDPs), a new, compact, factored model for domains...
Reinforcement learning (RL) algorithms provide a sound theoretical basis for building learning contr...
Markov decision processes (MDPs) have long been used to model qualitative aspects of systems in the ...
We present a class of metrics, defined on the state space of a finite Markov decision process (MDP)...
Learning, planning, and representing knowledge at multiple levels of temporal abstraction are key, l...
This paper provides new techniques for abstracting the state space of a Markov Decision Process (MD...
Learning, planning, and representing knowledge at multiple levels of temporal abstraction are key, l...
We study reinforcement learning for continuous-time Markov decision processes (MDPs) in the finite-h...
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