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Transferring knowledge among various environments is important to efficiently learn multiple tasks online.
Model-based reinforcement learning for atari
Kaiser, L.; Babaeizadeh, M.; Milos, P.; Osinski, B.; Campbell, R. H.; Czechowski, K.; Erhan, D.; Finn, C.; Kozakowski, P.; Levine, S.; et al. 2019 · 1903
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Model-Based Reinforcement Learning Exploiting State-Action Equivalence
Asadi, M.; Talebi, M. S.; Bourel, H.; and Maillard, O.-A. 2019 · 1910
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How Does an Approximate Model Help in Reinforcement Learning?
Feng, F.; Yin, W.; and Yang, L. F. 2019 · 1912
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Q-learning
Watkins, C. J.; and Dayan, P. 1992 · 1992
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Near-optimal reinforcement learning in polynomial time
Kearns, M.; and Singh, S. 2002 · 2002
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Multi-task reinforcement learning: a hierarchical Bayesian approach
Wilson, A.; Fern, A.; Ray, S.; and Tadepalli, P. 2007 · 2002
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R-max - a General Polynomial Time Algorithm for Near-optimal Reinforcement Learning
Brafman, R. I.; and Tennenholtz, M. 2003 · 2003
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Approximate equivalence of Markov decision processes
Even-Dar, E.; and Mansour, Y. 2003 · 2003
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On the sample complexity of reinforcement learning
Kakade, S. M.; et al. 2003 · 2003
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SMDP Homomorphisms: An Algebraic Approach to Abstraction in Semi-Markov Decision Processes
Ravindran, B.; and Barto, A. G. 2003 · 2003
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An algebraic approach to abstraction in reinforcement learning
Ravindran, B.; and Barto, A. G. 2004 · 2004
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Exploration via model based interval estimation
Strehl, A.; and Littman, M. 2004 · 2004
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A large-deviation inequality for vector-valued martingales
Hayes, T. P. 2005 · 2005
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Efficient Exploration With Latent Structure
Leffler, B. R.; Littman, M. L.; Strehl, A. L.; and Walsh, T. J. 2005 · 2005
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A Theoretical Analysis of Model-Based Interval Estimation
Strehl, A. L.; and Littman, M. L. 2005 · 2005
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Autonomous shaping: Knowledge transfer in reinforcement learning
Konidaris, G.; and Barto, A. 2006 · 2006
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Using homomorphisms to transfer options across continuous reinforcement learning domains
Soni, V.; and Singh, S. 2006 · 2006
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Incremental Model-Based Learners with Formal Learning-Time Guarantees
Strehl, A. L.; Li, L.; and Littman, M. L. 2006 · 2006
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Efficient reinforcement learning with relocatable action models
Leffler, B. R.; Littman, M. L.; and Edmunds, T. 2007 · 2007
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Transfer Learning in Real-Time Strategy Games Using Hybrid CBR/RL
Sharma, M.; Holmes, M. P.; Santamaría, J. C.; Irani, A.; Isbell Jr, C. L.; and Ram, A. 2007 · 2007
Directed Exploration in Reinforcement Learning with Transferred Knowledge
Mann, T. A.; and Choe, Y. 2012 · 2012
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Incremental model-based learners with formal learning-time guarantees
Strehl, A. L.; Li, L.; and Littman, M. L. 2012 · 2012
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Sample Complexity of Multi-Task Reinforcement Learning
Brunskill, E.; and Li, L. 2013 · 2013
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Clustering markov decision processes for continual transfer
Ramamoorthy, S.; Mahmud, M.; Hawasly, M.; and Rosman, B. 2013 · 2013
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PAC-Inspired Option Discovery in Lifelong Reinforcement Learning
Brunskill, E.; and Li, L. 2014 · 2014
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Autonomous cross-domain knowledge transfer in lifelong policy gradient reinforcement learning
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Representation Transfer for Reinforcement Learning
Taylor, M. E.; and Stone, P. 2007 · 2007
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CORL: A Continuous-State Offset-Dynamics Reinforcement Learner
Brunskill, E.; Leffler, B. R.; Li, L.; Littman, M. L.; and Roy, N. 2008 · 2008
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An analysis of model-based interval estimation for Markov decision processes
Strehl, A. L.; and Littman, M. L. 2008 · 2008
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Transfer via soft homomorphisms
Sorg, J.; and Singh, S. 2009 · 2009
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Transfer learning for reinforcement learning domains: A survey
Taylor, M. E.; and Stone, P. 2009 · 2009
Cited alongside, same era.
Near-optimal regret bounds for reinforcement learning
Jaksch, T.; Ortner, R.; and Auer, P. 2010 · 2010
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Ammar, H. B.; Eaton, E.; Luna, J. M.; and Ruvolo, P. 2015 · 2015
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OpenAI Gym
Brockman, G.; Cheung, V.; Pettersson, L.; Schneider, J.; Schulman, J.; Tang, J.; and Zaremba, W. 2016 · 2016
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PAC continuous state online multitask reinforcement learning with identification
Liu, Y.; Guo, Z.; and Brunskill, E. 2016 · 2016
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#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
Tang, H.; Houthooft, R.; Foote, D.; Stooke, A.; Chen, X.; Duan, Y.; Schulman, J.; De Turck, F.; and Abbeel, P. 2016 · 2016
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An optimal online method of selecting source policies for reinforcement learning
Li, S.; and Zhang, C. 2018 · 2018
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Markov decision processes with continuous side information
Modi, A.; Jiang, N.; Singh, S.; and Tewari, A. 2018 · 2018
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Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning
Nagabandi, A.; Kahn, G.; Fearing, R. S.; and Levine, S. 2018 · 2018
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Sequential Transfer in Reinforcement Learning with a Generative Model
Tirinzoni, A.; Poiani, R.; and Restelli, M. 2020 · 2020
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