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In this paper, we consider the problem of building learning agents that can efficiently learn to navigate in constrained environments.
A formal basis for the heuristic determination of minimum cost paths
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Probabilistic roadmaps for path planning in high-dimensional configuration spaces
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Human-level concept learning through probabilistic program induction
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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A. Kanezaki, J. Nitta, and Y. Sasaki · 2018
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Deeply informed neural sampling for robot motion planning
Ahmed H Qureshi and Michael C Yip · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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State Representation Learning for Control: An Overview
Timothée Lesort, Natalia Díaz-Rodríguez, Jean-François Goudou, and David Filliat · 2018
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Benchmarking Safe Exploration in Deep Reinforcement Learning
Alex Ray, Joshua Achiam, and Dario Amodei · 2019
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Safe reinforcement learning with model uncertainty estimates
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Densely connected convolutional networks
G. Huang, Z. Liu, L. v. d. Maaten, and K. Q. Weinberger · 2017
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Value iteration networks
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Learning navigation behaviors end-to-end with autorl
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Trajectory optimization for unknown constrained systems using reinforcement learning
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