[go: up one dir, main page]

Gregor et al., 2014 - Google Patents

Novelty detector for reinforcement learning based on forecasting

Gregor et al., 2014

Document ID
17979173510913255563
Author
Gregor M
Spalek J
Publication year
Publication venue
2014 IEEE 12th International Symposium on Applied Machine Intelligence and Informatics (SAMI)

External Links

Snippet

The paper proposes a novelty detector based on an artificial neural network forecaster. It shows how such forecaster can be constructed and as a novelty detector. Two variations of the forecaster are presented-one is based on backpropagation, and the other on Rprop. It is …
Continue reading at ieeexplore.ieee.org (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
    • 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
    • G06N3/06Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G06N3/0635Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/04Inference methods or devices
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/12Computer systems based on biological models using genetic models
    • G06N3/126Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • 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
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/02Knowledge representation
    • G06N5/022Knowledge engineering, knowledge acquisition
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00Computer systems based on specific mathematical models
    • G06N7/005Probabilistic networks
    • 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
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management

Similar Documents

Publication Publication Date Title
Padakandla et al. Reinforcement learning algorithm for non-stationary environments
Yang et al. Hierarchical deep reinforcement learning for continuous action control
dos Santos et al. Reactive search strategies using reinforcement learning, local search algorithms and variable neighborhood search
Rakitianskaia et al. Training feedforward neural networks with dynamic particle swarm optimisation
Kujanpää et al. Hierarchical imitation learning with vector quantized models
CN107967513B (en) Multirobot intensified learning collaboratively searching method and system
Bahle et al. Lifelong learning and collaboration of smart technical systems in open-ended environments--Opportunistic collaborative interactive learning
Hafez et al. Topological Q-learning with internally guided exploration for mobile robot navigation
Ngo et al. Confidence-based progress-driven self-generated goals for skill acquisition in developmental robots
Gregor et al. Novelty detector for reinforcement learning based on forecasting
Leventi-Peetz et al. Scope and sense of explainability for ai-systems
Othmani-Guibourg et al. LSTM Path-Maker: a new LSTM-based strategy for the multi-agent patrolling
Zhang et al. Clique-based cooperative multiagent reinforcement learning using factor graphs
Fernandez-Gauna et al. Undesired state-action prediction in multi-agent reinforcement learning for linked multi-component robotic system control
Sherstan Representation and general value functions
Pan et al. A Survey of Continual Reinforcement Learning
CN113779396B (en) Question recommending method and device, electronic equipment and storage medium
García et al. Incremental reinforcement learning for multi-objective robotic tasks
Cawalla et al. Graph Reinforcement Learning for Courses of Action Analysis
Waldock et al. Learning a robot controller using an adaptive hierarchical fuzzy rule-based system
Hwang et al. Induced states in a decision tree constructed by Q-learning
Raja Reinforcement learning in dynamic environments: challenges and future directions
Macedo et al. Genetic programming algorithms for dynamic environments
Osawa et al. An implementation of working memory using stacked half restricted Boltzmann machine: Toward to restricted Boltzmann machine-based cognitive architecture
Taheri Yeganeh et al. Active Inference Meeting Energy-Efficient Control of Parallel and Identical Machines