Fetching the paper…
Reading the bibliography…
Reinforcement learning algorithms rely on carefully engineering environment rewards that are extrinsic to the agent.
Curious model-building control systems
J. Schmidhuber · 1991
Earlier work this paper cites.
Reinforcement learning: An introduction
R. S. Sutton and A. G. Barto · 1998
Earlier work this paper cites.
Intrinsic and extrinsic motivations: Classic definitions and new directions
E. L. Ryan, Richard; Deci · 2000
Earlier work this paper cites.
Mda: A formal approach to game design and game research
R. Hunicke, M. LeBlanc, and R. Zubek · 2004
Earlier work this paper cites.
Why we play games: Four keys to more emotion in player experiences
N. Lazzaro · 2004
Earlier work this paper cites.
Intrinsically motivated reinforcement learning
S. P. Singh, A. G. Barto, and N. Chentanez · 2005
Earlier work this paper cites.
The development of embodied cognition: Six lessons from babies
L. Smith and M. Gasser · 2005
Earlier work this paper cites.
An analytic solution to discrete bayesian reinforcement learning
P. Poupart, N. Vlassis, J. Hoey, and K. Regan · 2006
Earlier work this paper cites.
Exploiting open-endedness to solve problems through the search for novelty
J. Lehman and K. O. Stanley · 2008
Earlier work this paper cites.
What is the best multi-stage architecture for object recognition?
K. Jarrett, K. Kavukcuoglu, Y. LeCun, et al · 2009
Earlier work this paper cites.
What is intrinsic motivation? a typology of computational approaches
P.-Y. Oudeyer and F. Kaplan · 2009
Earlier work this paper cites.
Formal theory of creativity, fun, and intrinsic motivation (1990–2010)
J. Schmidhuber · 2010
Earlier work this paper cites.
Abandoning objectives: Evolution through the search for novelty alone
J. Lehman and K. O. Stanley · 2011
Earlier work this paper cites.
On random weights and unsupervised feature learning
A. M. Saxe, P. W. Koh, Z. Chen, M. Bhand, B. Suresh, and A. Y. Ng · 2011
Earlier work this paper cites.
The role of game discourse analysis and curiosity in creating engaging and effective serious games by implementing a back story and foreshadowing
P. Wouters, H. Van Oostendorp, R. Boonekamp, and E. Van der Spek · 2011
Cited alongside, same era.
An information-theoretic approach to curiosity-driven reinforcement learning
S. Still and D. Precup · 2012
Cited alongside, same era.
The arcade learning environment: An evaluation platform for general agents
M. G. Bellemare, Y. Naddaf, J. Veness, and M. Bowling · 2013
Cited alongside, same era.
Uncertainty in games
G. Costikyan · 2013
Cited alongside, same era.
Auto-encoding variational Bayes
D. P. Kingma and M. Welling · 2013
Cited alongside, same era.
Stochastic backpropagation and approximate inference in deep generative models
Deep exploration via bootstrapped dqn
I. Osband, C. Blundell, A. Pritzel, and B. Van Roy · 2016
Later among the works it cites.
Surprise-based intrinsic motivation for deep reinforcement learning
J. Achiam and S. Sastry · 2017
Later among the works it cites.
UCB and infogain exploration via q q -ensembles
R. Y. Chen, J. Schulman, P. Abbeel, and S. Sidor · 2017
Later among the works it cites.
Noisy networks for exploration
M. Fortunato, M. G. Azar, B. Piot, J. Menick, I. Osband, A. Graves, V. Mnih, R. Munos, D. Hassabis, O. Pietquin, C. Blundell, and S. Legg · 2017
Later among the works it cites.
EX2: Exploration with exemplar models for deep reinforcement learning
J. Fu, J. D. Co-Reyes, and S. Levine · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
D. J. Rezende, S. Mohamed, and D. Wierstra · 2014
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Cited alongside, same era.
Human-level control through deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al · 2015
Cited alongside, same era.
Variational information maximisation for intrinsically motivated reinforcement learning
S. Mohamed and D. J. Rezende · 2015
Cited alongside, same era.
Incentivizing exploration in reinforcement learning with deep predictive models
B. C. Stadie, S. Levine, and P. Abbeel · 2015
Cited alongside, same era.
Why greatness cannot be planned: The myth of the objective
K. O. Stanley and J. Lehman · 2015
Cited alongside, same era.
Deep fried convnets
Z. Yang, M. Moczulski, M. Denil, N. de Freitas, A. Smola, L. Song, and Z. Wang · 2015
Cited alongside, same era.
Variational intrinsic control
K. Gregor, D. J. Rezende, and D. Wierstra · 2017
Later among the works it cites.
Count-based exploration with neural density models
G. Ostrovski, M. G. Bellemare, A. v. d. Oord, and R. Munos · 2017
Later among the works it cites.
Curiosity-driven exploration by self-supervised prediction
D. Pathak, P. Agrawal, A. A. Efros, and T. Darrell · 2017
Later among the works it cites.
Parameter space noise for exploration
M. Plappert, R. Houthooft, P. Dhariwal, S. Sidor, R. Y. Chen, X. Chen, T. Asfour, P. Abbeel, and M. Andrychowicz · 2017
Later among the works it cites.
#Exploration: A study of count-based exploration for deep reinforcement learning
H. Tang, R. Houthooft, D. Foote, A. Stooke, X. Chen, Y. Duan, J. Schulman, F. De Turck, and P. Abbeel · 2017
Later among the works it cites.
Diversity is all you need: Learning skills without a reward function
B. Eysenbach, A. Gupta, J. Ibarz, and S. Levine · 2018
Closest in time.
Computational theories of curiosity-driven learning
P.-Y. Oudeyer · 2018
Closest in time.
Zero-shot visual imitation
D. Pathak, P. Mahmoudieh, G. Luo, P. Agrawal, D. Chen, Y. Shentu, E. Shelhamer, J. Malik, A. A. Efros, and T. Darrell · 2018
Closest in time.
Intrinsic motivation and automatic curricula via asymmetric self-play
S. Sukhbaatar, I. Kostrikov, A. Szlam, and R. Fergus · 2018
Closest in time.