Deprecated: The each() function is deprecated. This message will be suppressed on further calls in /home/zhenxiangba/zhenxiangba.com/public_html/phproxy-improved-master/index.php on line 456
Montgomery et al., 2017 - Google Patents
[go: Go Back, main page]

Montgomery et al., 2017 - Google Patents

Reset-free guided policy search: Efficient deep reinforcement learning with stochastic initial states

Montgomery et al., 2017

View PDF
Document ID
561241442632502531
Author
Montgomery W
Ajay A
Finn C
Abbeel P
Levine S
Publication year
Publication venue
2017 IEEE International Conference on Robotics and Automation (ICRA)

External Links

Snippet

Autonomous learning of robotic skills can allow general-purpose robots to learn wide behavioral repertoires without extensive manual engineering. However, robotic skill learning must typically make trade-offs to enable practical real-world learning, such as requiring …
Continue reading at arxiv.org (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/04Architectures, e.g. interconnection topology
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/08Learning methods
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00Systems involving the use of models or simulators of said systems
    • G05B17/02Systems involving the use of models or simulators of said systems electric
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6288Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion
    • G06K9/629Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion of extracted features

Similar Documents

Publication Publication Date Title
Montgomery et al. Reset-free guided policy search: Efficient deep reinforcement learning with stochastic initial states
Gu et al. Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
Yahya et al. Collective robot reinforcement learning with distributed asynchronous guided policy search
Kahn et al. Plato: Policy learning using adaptive trajectory optimization
Chebotar et al. Closing the sim-to-real loop: Adapting simulation randomization with real world experience
Vecerik et al. A practical approach to insertion with variable socket position using deep reinforcement learning
Kaushik et al. Fast online adaptation in robotics through meta-learning embeddings of simulated priors
Jain et al. Learning deep visuomotor policies for dexterous hand manipulation
CN113677485B (en) Efficient Adaptation of Robot Control Strategies for New Tasks Using Meta-learning Based on Meta-imitation Learning and Meta-reinforcement Learning
Laskey et al. Comparing human-centric and robot-centric sampling for robot deep learning from demonstrations
Laskey et al. Dart: Noise injection for robust imitation learning
Zhang et al. Learning deep neural network policies with continuous memory states
US11403513B2 (en) Learning motor primitives and training a machine learning system using a linear-feedback-stabilized policy
Finn et al. Deep visual foresight for planning robot motion
Fu et al. One-shot learning of manipulation skills with online dynamics adaptation and neural network priors
Sæmundsson et al. Meta reinforcement learning with latent variable gaussian processes
Amarjyoti Deep reinforcement learning for robotic manipulation-the state of the art
Ren et al. Adaptsim: Task-driven simulation adaptation for sim-to-real transfer
Si et al. Agen: Adaptable generative prediction networks for autonomous driving
Bischoff et al. Policy search for learning robot control using sparse data
Fanger et al. Gaussian processes for dynamic movement primitives with application in knowledge-based cooperation
Tschiatschek et al. Variational inference for data-efficient model learning in pomdps
CN119501923A (en) Human-in-the-loop tasks and motion planning in imitation learning
Alt et al. Robot program parameter inference via differentiable shadow program inversion
Torabi et al. Sample-efficient adversarial imitation learning from observation