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Abstract: A large class of sequential decision-making problems under uncertainty can be modeled as Markov and Semi-Markov Decision Problems, when their underlying probability structure has a Markov chain. They may be solved by using classical dynamic programming methods. However, dynamic programming methods su#er from the curse of dimensionality and break down rapidly in face of large state spaces. In addition, dynamic programming methods require the exact computation of the so-called transition... (Update)
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BibTeX entry: (Update)
@misc{ assistant-reinforcement,
author = "Abhijit Gosavi Assistant",
title = "Reinforcement Learning for Long-Run Average Cost",
url = "citeseer.ist.psu.edu/757585.html" }
Citations (may not include all citations):658 Learning from Delayed Rewards (context) - Watkins - 1989
413 Neuro-Dynamic Programming (context) - Bertsekas, Tsitsiklis - 1996
246 Markov Decision Processes (context) - Puterman - 1994
189 Reinforcement Learning (context) - Sutton, Barto - 1998
139 A stochastic approximation method (context) - Robbins, Monro - 1951
115 Stochastic Approximation Methods for Constrained and Unconst.. (context) - Kushner, Clark - 1978
92 Learning Automata: An Introduction (context) - Narendra, Thatachar - 1989
71 Asynchronous stochastic approximation and q-learning (context) - Tsitsiklis - 1994
64 Algorithms for sequential decision-making - Littman - 1996
61 Analysis of recursive stochastic algorithms (context) - Ljung - 1977
52 A reinforcement learning method for maximizing undiscounted .. (context) - Schwartz - 1993
48 Average reward reinforcement learning: Foundations - Mahadevan - 1996
26 Stochastic approximation with two-time scales (context) - Borkar - 1997
24 Dynamic Programming and Optimal Control (context) - Bertsekas - 1995
23 Reinforcement learning algorithms for average-payo# Markovia.. - Singh - 1994
20 Scaling up average reward reinforcement learning by approxim.. - Tadepalli, Ok - 1996
16 Asynchronous stochastic approximation - Borkar - 1998
14 Actor-critic type learning algorithms for markov decision pr.. (context) - Konda, Borkar
12 Stochastic Dynamic Programming and the Control of Queueing S.. (context) - Sennott - 1999
9 The theory of dynamic programming (context) - Bellman - 1954
8 Solving semi-markov decision problems using average reward r.. - Das, Gosavi et al. - 1999
7 The ode method for convergence of stochastic approximation a.. - Borkar, Meyn
6 Introduction to Reliability Engineering (context) - Lewis - 1994
3 Airline seat allocation among Multiple Fare Classes with Ove.. (context) - Gosavi, Bandla et al.
3 Ode analysis for Q-learning algorithms (context) - Abounadi, Bertsekas et al. - 1996
3 Optimal preventive maintenance in a production inventory sys.. (context) - Das, Sarkar
2 Optimal control of a queuing network system with two types o.. (context) - Shioyama - 1991
2 the Convergence of Some Reinforcement Learning Algorithms (context) - Gosavi - 2000
2 An analog scheme for fixed point computation (context) - Borkar, Soumyanath - 1997
2 A Simulation-Based Learning Automata Framework for Solving S.. (context) - Gosavi, Das et al.
2 Part selection policy for a flexible manufacturing cell feed.. (context) - Seidmann, Schweitzer - 1984
2 An algorithm for solving semi-markov decision problems using.. (context) - Gosavi - 1999
2 Optimal and near-optimal control of a two-part stochastic ma.. (context) - Elhafsi, Bai - 1997
1 Special Issue of Machine Learning Journal (context) - Sutton - 1992
1 Optimal inspection policies for a manufacturing station (context) - Cassandras, Han - 1992
Documents on the same site (http://www.eng.buffalo.edu/~agosavi/papers.html): More
Global Supply Chain Management: A Reinforcement.. - Pontrandolfo, Gosavi, .. (2002) (Correct)
Stochasticsand - Statistics Reinforcement Learning (Correct)
Boundedness of Iterates in Q-Learning - Abhijit Gosavi Department (Correct)
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