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ICANN
2001
Springer
15 years 11 months ago
Market-Based Reinforcement Learning in Partially Observable Worlds
Unlike traditional reinforcement learning (RL), market-based RL is in principle applicable to worlds described by partially observable Markov Decision Processes (POMDPs), where an ...
Ivo Kwee, Marcus Hutter, Jürgen Schmidhuber
COLT
2000
Springer
15 years 11 months ago
Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning
We model reinforcement learning as the problem of learning to control a Partially Observable Markov Decision Process (  ¢¡¤£¦¥§  ), and focus on gradient ascent approache...
Peter L. Bartlett, Jonathan Baxter
AAAI
2006
15 years 8 months ago
Factored MDP Elicitation and Plan Display
The software suite we will demonstrate at AAAI '06 was designed around planning with factored Markov decision processes (MDPs). It is a user-friendly suite that facilitates d...
Krol Kevin Mathias, Casey Lengacher, Derek William...
AIPS
2006
15 years 8 months ago
Automated Planning Using Quantum Computation
This paper presents an adaptation of the standard quantum search technique to enable application within Dynamic Programming, in order to optimise a Markov Decision Process. This i...
Sanjeev Naguleswaran, Langford B. White, I. Fuss
AIPS
2003
15 years 8 months ago
Synthesis of Hierarchical Finite-State Controllers for POMDPs
We develop a hierarchical approach to planning for partially observable Markov decision processes (POMDPs) in which a policy is represented as a hierarchical finite-state control...
Eric A. Hansen, Rong Zhou