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Reinforcement learning (RL), particularly in sparse reward settings, often requires prohibitively large numbers of interactions with the environment, thereby limiting its applicability to complex problems.
Policy invariance under reward transformations: Theory and application to reward shaping
A. Y. Ng, D. Harada, and S. Russell · 1999
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Guiding a reinforcement learner with natural language advice: Initial results in robocup soccer
G. Kuhlmann, P. Stone, R. Mooney, and J. Shavlik · 2004
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A survey of robot learning from demonstration
B. D. Argall, S. Chernova, M. Veloso, and B. Browning · 2009
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Understanding natural language commands for robotic navigation and mobile manipulation
S. Tellex, T. Kollar, S. Dickerson, M. R. Walter, A. G. Banerjee, S. J. Teller, and N. Roy · 2011
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A survey of inverse reinforcement learning techniques
Y. Gao, J. Peters, A. Tsourdos, S. Zhifei, and E. M. Joo · 2012
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Learning to win by reading manuals in a monte-carlo framework
S. Branavan, D. Silver, and R. Barzilay · 2012
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Learning high-level planning from text
S. Branavan, N. Kushman, T. Lei, and R. Barzilay · 2012
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Training an agent to ground commands with reward and punishment
J. MacGlashan, M. Littman, R. Loftin, B. Peng, D. Roberts, and M. E. Taylor · 2014
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Language understanding for text-based games using deep reinforcement learning
K. Narasimhan, T. Kulkarni, and R. Barzilay · 2015
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Accurately and efficiently interpreting human-robot instructions of varying granularities
D. Arumugam, S. Karamcheti, N. Gopalan, L. L. Wong, and S. Tellex · 2017
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Beating atari with natural language guided reinforcement learning
R. Kaplan, C. Sauer, and A. Sosa · 2017
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Guiding reinforcement learning exploration using natural language
B. Harrison, U. Ehsan, and M. O. Riedl · 2017
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Reward shaping in episodic reinforcement learning
M. Grzes · 2017
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Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
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Learning to parse natural language to grounded reward functions with weak supervision
E. C. Williams, N. Gopalan, M. Rhee, and S. Tellex · 2018
Cited alongside, same era.
A survey of reinforcement learning informed by natural language
J. Luketina, N. Nardelli, G. Farquhar, J. Foerster, J. Andreas, E. Grefenstette, S. Whiteson, and T. Rocktaschel · 2019
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Using natural language for reward shaping in reinforcement learning
P. Goyal, S. Niekum, and R. J. Mooney · 2019
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Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
T. Yu, D. Quillen, Z. He, R. Julian, K. Hausman, C. Finn, and S. Levine · 2019
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Grounding natural language commands to starcraft ii game states for narration-guided reinforcement learning
N. Waytowich, S. L. Barton, V. Lawhern, E. Stump, and G. Warnell · 2019
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From language to goals: Inverse reinforcement learning for vision-based instruction following
J. Fu, A. Korattikara, S. Levine, and S. Guadarrama · 2019
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D. Bahdanau, F. Hill, J. Leike, E. Hughes, A. Hosseini, P. Kohli, and E. Grefenstette · 2018
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Robobarista: Object part based transfer of manipulation trajectories from crowd-sourcing in 3d pointclouds
J. Sung, S. H. Jin, and A. Saxena · 2018
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Mapping instructions to actions in 3D environments with visual goal prediction
D. Misra, A. Bennett, V. Blukis, E. Niklasson, M. Shatkhin, and Y. Artzi · 2018
Cited alongside, same era.
Learning with latent language
J. Andreas, D. Klein, and S. Levine · 2018
Cited alongside, same era.
Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments
P. Anderson, Q. Wu, D. Teney, J. Bruce, M. Johnson, N. Sünderhauf, I. Reid, S. Gould, and A. van den Hengel · 2018
Cited alongside, same era.
Learning to map natural language instructions to physical quadcopter control using simulated flight
V. Blukis, Y. Terme, E. Niklasson, R. A. Knepper, and Y. Artzi · 2019
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Prospection: Interpretable plans from language by predicting the future
C. Paxton, Y. Bisk, J. Thomason, A. Byravan, and D. Fox · 2019
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A review of robot learning for manipulation: Challenges, representations, and algorithms
O. Kroemer, S. Niekum, and G. Konidaris · 2019
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Robots that use language
S. Tellex, N. Gopalan, H. Kress-Gazit, and C. Matuszek · 2020
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C. Lynch and P. Sermanet · 2020
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