Fetching the paper…
Reading the bibliography…
Real-world reinforcement learning tasks often involve some form of partial observability where the observations only give a partial or noisy view of the true state of the world.
Q-learning
Christopher J. C. H. Watkins and Peter Dayan · 1992
Earlier work this paper cites.
Learning policies for partially observable environments: Scaling up, 1995
Michael L. Littman, Anthony R. Cassandra, and Leslie Pack Kaelbling · 1995
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Solving large pomdps using real time dynamic programming
Blai Bonet · 1998
Earlier work this paper cites.
Planning and acting in partially observable stochastic domains
Leslie Pack Kaelbling, Michael L Littman, and Anthony R Cassandra · 1998
Earlier work this paper cites.
The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
Earlier work this paper cites.
On the properties of neural machine translation: Encoder-decoder approaches
Kyunghyun Cho, Bart Van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio · 2014
Earlier work this paper cites.
Deep recurrent q-learning for partially observable mdps
Matthew Hausknecht and Peter Stone · 2015
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Earlier work this paper cites.
Deep attention recurrent q-network
Ivan Sorokin, Alexey Seleznev, Mikhail Pavlov, Aleksandr Fedorov, and Anastasiia Ignateva · 2015
Earlier work this paper cites.
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
Earlier work this paper cites.
Learning to communicate to solve riddles with deep distributed recurrent q-networks
Jakob N Foerster, Yannis M Assael, Nando de Freitas, and Shimon Whiteson · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Vizdoom: A doom-based ai research platform for visual reinforcement learning
Michał Kempka, Marek Wydmuch, Grzegorz Runc, Jakub Toczek, and Wojciech Jaśkowski · 2016
Earlier work this paper cites.
Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
Earlier work this paper cites.
Deep reinforcement learning with double q-learning
Hado Van Hasselt, Arthur Guez, and David Silver · 2016
Earlier work this paper cites.
Playing fps games with deep reinforcement learning
Guillaume Lample and Devendra Singh Chaplot · 2017
Earlier work this paper cites.
A simple neural attentive meta-learner
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel · 2017
Earlier work this paper cites.
Asymmetric actor critic for image-based robot learning
Lerrel Pinto, Marcin Andrychowicz, Peter Welinder, Wojciech Zaremba, and Pieter Abbeel · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
On improving deep reinforcement learning for pomdps
Pengfei Zhu, Xin Li, Pascal Poupart, and Guanghui Miao · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Cited alongside, same era.
Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke Hoof, and David Meger · 2018
Cited alongside, same era.
Transformer based reinforcement learning for games
Uddeshya Upadhyay, Nikunj Shah, Sucheta Ravikanti, and Mayanka Medhe · 2019
Later among the works it cites.
Attention is not not explanation
Sarah Wiegreffe and Yuval Pinter · 2019
Later among the works it cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Later among the works it cites.
Robotic table tennis with model-free reinforcement learning
Wenbo Gao, Laura Graesser, Krzysztof Choromanski, Xingyou Song, Nevena Lazic, Pannag Sanketi, Vikas Sindhwani, and Navdeep Jaitly · 2020
Later among the works it cites.
Discriminative particle filter reinforcement learning for complex partial observations
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Soft actor-critic algorithms and applications
Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, et al · 2018
Cited alongside, same era.
Rainbow: Combining improvements in deep reinforcement learning
Matteo Hessel, Joseph Modayil, Hado Van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, and David Silver · 2018
Cited alongside, same era.
Deep variational reinforcement learning for pomdps
Maximilian Igl, Luisa Zintgraf, Tuan Anh Le, Frank Wood, and Shimon Whiteson · 2018
Cited alongside, same era.
Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
Sergey Levine, Peter Pastor, Alex Krizhevsky, Julian Ibarz, and Deirdre Quillen · 2018
Cited alongside, same era.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever · 2018
Cited alongside, same era.
Reinforcement Learning: An Introduction
Richard S. Sutton and Andrew G. Barto · 2018
Cited alongside, same era.
Character-level language modeling with deeper self-attention
Rami Al-Rfou, Dokook Choe, Noah Constant, Mandy Guo, and Llion Jones · 2019
Cited alongside, same era.
Xiao Ma, Peter Karkus, David Hsu, Wee Sun Lee, and Nan Ye · 2020
Later among the works it cites.
Stabilizing transformers for reinforcement learning
Emilio Parisotto, Francis Song, Jack Rae, Razvan Pascanu, Caglar Gulcehre, Siddhant Jayakumar, Max Jaderberg, Raphael Lopez Kaufman, Aidan Clark, Seb Noury, et al · 2020
Later among the works it cites.
Yu-An Wang and Yun-Nung Chen · 2020
Later among the works it cites.
Deep reinforcement learning with transformers for text adventure games
Yunqiu Xu, Ling Chen, Meng Fang, Yang Wang, and Chengqi Zhang · 2020
Later among the works it cites.
Big bird: Transformers for longer sequences
Manzil Zaheer, Guru Guruganesh, Kumar Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, et al · 2020
Later among the works it cites.
gym-gridverse: Gridworld domains for fully and partially observable reinforcement learning
Andrea Baisero and Sammie Katt · 2021
Later among the works it cites.
Decision transformer: Reinforcement learning via sequence modeling
Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Misha Laskin, Pieter Abbeel, Aravind Srinivas, and Igor Mordatch · 2021
Later among the works it cites.
Stabilizing deep q-learning with convnets and vision transformers under data augmentation
Nicklas Hansen, Hao Su, and Xiaolong Wang · 2021
Later among the works it cites.
Offline reinforcement learning as one big sequence modeling problem
Michael Janner, Qiyang Li, and Sergey Levine · 2021
Later among the works it cites.
Pomdp robot domains
Hai Nguyen · 2021
Later among the works it cites.
Recurrent model-free rl can be a strong baseline for many pomdps
Tianwei Ni, Benjamin Eysenbach, and Ruslan Salakhutdinov · 2021
Later among the works it cites.
Recurrent off-policy baselines for memory-based continuous control
Zhihan Yang and Hai Huu Nguyen · 2021
Later among the works it cites.
Towards flexible inference in sequential decision problems via bidirectional transformers
Micah Carroll, Jessy Lin, Orr Paradise, Raluca Georgescu, Mingfei Sun, David Bignell, Stephanie Milani, Katja Hofmann, Matthew Hausknecht, Anca Dragan, et al · 2022
Closest in time.
Qinqing Zheng, Amy Zhang, and Aditya Grover · 2022
Closest in time.