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A Real-Time Model-Based Reinforcement Learning Architecture for Robot Control
[article]
2011
arXiv
pre-print
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In this paper, we present a novel parallel architecture for model-based RL that runs in real-time by 1) taking advantage of sample-based approximate planning methods and 2) parallelizing the acting, model ...
Reinforcement Learning (RL) is a method for learning decision-making tasks that could enable robots to learn and adapt to their situation on-line. ...
ACKNOWLEDGMENTS This work has taken place in the Learning Agents Research Group (LARG) at the Artificial Intelligence Laboratory, The University of Texas at Austin. ...
arXiv:1105.1749v2
fatcat:7j7dnqntt5ac5p7oej5vk5obxa
RTMBA: A Real-Time Model-Based Reinforcement Learning Architecture for robot control
2012
2012 IEEE International Conference on Robotics and Automation
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In this paper, we present a novel parallel architecture for model-based RL that runs in real-time by 1) taking advantage of sample-based approximate planning methods and 2) parallelizing the acting, model ...
For an RL algorithm to be practical for robotic control tasks, it must learn in very few samples, while continually taking actions in real-time. ...
Fig. 2 . 2 A diagram of the Real-Time Model Based Architecture (RTMBA)
Average reward versus clock time. ...
doi:10.1109/icra.2012.6225072
dblp:conf/icra/HesterQS12
fatcat:ifxetlh4qnaz5h6n5degashuxy
The Open-Source TEXPLORE Code Release for Reinforcement Learning on Robots
[chapter]
2014
Lecture Notes in Computer Science
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For an RL algorithm to be practical for robotic control tasks, it must learn in very few samples, while continually taking actions in real-time. ...
We demonstrate texplore learning to control the velocity of an autonomous vehicle in real-time. texplore has been released as an open-source ROS repository, enabling learning on a variety of robot tasks ...
Acknowledgements This work has taken place in the Learning Agents Research Group (LARG) at UT Austin. ...
doi:10.1007/978-3-662-44468-9_47
fatcat:75j3f6puhjbgdpbugarlmd2fim
TEXPLORE: real-time sample-efficient reinforcement learning for robots
2012
Machine Learning
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For an RL algorithm to be practical for robotic control tasks, it must learn in very few samples, while continually taking actions in real-time. ...
With sample-based planning and a novel parallel architecture, TEXPLORE can select actions continually in real-time whenever necessary. ...
Parallel Architecture In addition to using MCTS for planning, we have developed a multi-threaded architecture, called the Real-Time Model Based Architecture (RTMBA), for the agent to learn while acting ...
doi:10.1007/s10994-012-5322-7
fatcat:5bac6cigxbgnzg6krsrbq2ywba
Continuous reinforcement learning with incremental Gaussian mixture models
2019
Figshare
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Then, this same function approximator was employed to model the joint state and Q-values space, all in a single FIGMN, resulting in a concise and data-efficient algorithm, i.e., a reinforcement learning ...
This algorithm, called Fast Incremental Gaussian Mixture Network (FIGMN), was employed as a sample-efficient function approximator for the state space of continuous reinforcement learning tasks, which, ...
RTMBA Real-Time Model-Based Architecture (RTMBA) [Hester, Quinlan and Stone 2012] aims to make model-based reinforcement learning practical for real-time applications like robotics. ...
doi:10.6084/m9.figshare.9942521
fatcat:jly4alfvtjgy7ktm7pheayvwni
Intrinsically motivated model learning for developing curious robots
2017
Artificial Intelligence
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Reinforcement Learning (RL) agents are typically deployed to learn a specific, concrete task based on a pre-defined reward function. ...
Novel methods for obtaining intrinsic rewards from a random-forest-based model of the world. 2. ...
In order to be applicable to robots, texplore-vanir uses the Real-Time Model Based Architecture [24] . ...
doi:10.1016/j.artint.2015.05.002
fatcat:uthcqhezpzgdpnlpflpizugqrq
Intrinsically motivated model learning for a developing curious agent
2012
2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL)
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Reinforcement Learning (RL) agents are typically deployed to learn a specific, concrete task based on a pre-defined reward function. ...
The algorithm learns models of the transition dynamics of a domain using random forests. ...
In order to be applicable to robots, TEXPLORE-VANIR uses the Real-Time Model Based Architecture [4] . ...
doi:10.1109/devlrn.2012.6400802
dblp:conf/icdl-epirob/HesterS12
fatcat:jke4h6mpsrgl3hrmp42bqukp4i
Flexible Heuristic Dynamic Programming for Reinforcement Learning in Quad-Rotors
2018
2018 AIAA Information Systems-AIAA Infotech @ Aerospace
unpublished
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Figure 2 . 8 : 28 Algorithm for the Real-Time Model-Based Architecture. Taken from[27]. ...
In [27] , a Real-Time Model Based Architecture (RTMBA) for RL is proposed that parallelizes the modellearning, planning and action within an agent into three separate threads that can be run on separate ...
cr do set trace of nearest neighbors to 1 17: z t (p) ← 1 18: for p in M cr do update values of critic nearest neighbors 19 : add [s t , a t |s t +1 ] to M pm add sample to plant model memory 21 ...
doi:10.2514/6.2018-2134
fatcat:o6ttgzc5drcoxnu7owmbhdhulq