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ICML
1998
IEEE
16 years 6 months ago
The MAXQ Method for Hierarchical Reinforcement Learning
This paper presents a new approach to hierarchical reinforcement learning based on the MAXQ decomposition of the value function. The MAXQ decomposition has both a procedural seman...
Thomas G. Dietterich
AAAI
2010
15 years 7 months ago
Reinforcement Learning via AIXI Approximation
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...
Joel Veness, Kee Siong Ng, Marcus Hutter, David Si...
FLAIRS
1998
15 years 7 months ago
Optimizing Production Manufacturing Using Reinforcement Learning
Manyindustrial processes involve makingparts with an assemblyof machines, where each machinecarries out an operation on a part, and the finished product requires a wholeseries of ...
Sridhar Mahadevan, Georgios Theocharous
ICML
2003
IEEE
16 years 6 months ago
Action Elimination and Stopping Conditions for Reinforcement Learning
We consider incorporating action elimination procedures in reinforcement learning algorithms. We suggest a framework that is based on learning an upper and a lower estimates of th...
Eyal Even-Dar, Shie Mannor, Yishay Mansour
ECML
2006
Springer
15 years 9 months ago
Scaling Model-Based Average-Reward Reinforcement Learning for Product Delivery
Reinforcement learning in real-world domains suffers from three curses of dimensionality: explosions in state and action spaces, and high stochasticity. We present approaches that ...
Scott Proper, Prasad Tadepalli