Recurrent Reinforcement Learning: A Hybrid Approach

Reinforcement Learning for Robots using Neural Networks

Lin, Long-Ji · 1993

Earlier work this paper cites.

Temporal difference learning and TD-Gammon

Tesauro, Gerald · 1995

Earlier work this paper cites.

Customer lifetime valuation to support marketing decision making

Dwyer, F. Robert · 1997

Earlier work this paper cites.

Long short-term memory

Hochreiter, Sepp and Schmidhuber, Jürgen · 1997

Earlier work this paper cites.

Planning and acting in partially observable stochastic domains

Kaelbling, Leslie Pack, Littman, Michael L., and Cassandra, Anthony R · 1998

Earlier work this paper cites.

Reinforcement Learning: An Introduction

Sutton, Richard S. and Barto, Andrew G · 1998

Earlier work this paper cites.

Reinforcement learning with long short-term memory

Bakker, Bram · 2002

Earlier work this paper cites.

Predictive representations of state

Littman, Michael L., Sutton, Richard S., and Singh, Satinder P · 2002

Earlier work this paper cites.

Sequential cost-sensitive decision-making with reinforcement learning

Pednault, Edwin P. D., Abe, Naoki, and Zadrozny, Bianca · 2002

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Point-based value iteration: An anytime algorithm for POMDPs

Pineau, Joelle, Gordon, Geoffrey J., and Thrun, Sebastian · 2003

Cited alongside, same era.

Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management

Berry, Michael J.A. and Linoff, Gordon S · 2004

Cited alongside, same era.

Partially observable Markov decision processes for spoken dialog systems

Original

Williams, Jason D. and Young, Steve J · 2007

Cited alongside, same era.

A hidden Markov model of customer relationship dynamics

Netzer, Oded, Lattin, James M., and Srinivasan, V · 2008

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Closing the learning-planning loop with predictive state representations

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Silver, David, Newnham, Leonard, Barker, David, Weller, Suzanne, and McFall, Jason · 2013

Later among the works it cites.

Deep learning: Methods and applications

Deng, Li and Yu, Dong · 2014

Later among the works it cites.

Deep recurrent Q-learning for partially observable MDPs, 2015

Original

Hausknecht, Matthew and Stone, Peter · 2015

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Improved Empirical Methods in Reinforcement-Learning Evaluation

Marivate, Vukosi N · 2015

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Human-level control through deep reinforcement learning

Mnih, Volodymyr, Kavukcuoglu, Koray, Silver, David, Rusu, Andrei A., Veness, Joel, Bellemare, Marc G., Graves, Alex, Riedmiller, Martin, Fidjeland, Andreas K., Ostrovski, Georg, Petersen, Stig, Beattie, Charles, Sadik, Amir, Antonoglou, Ioannis, King, Helen, Kumaran, Dharshan, Wierstra, Daan, Legg, Shane, and Hassabis, Demis · 2015

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