+

Ozdaglar et al., 2021 - Google Patents

Independent learning in stochastic games

Ozdaglar et al., 2021

View PDF
Document ID
11926321031325377223
Author
Ozdaglar A
Sayin M
Zhang K
Publication year
Publication venue
International Congress of Mathematicians

External Links

Snippet

Reinforcement learning (RL) has recently achieved tremendous successes in many artificial intelligence applications. Many of the forefront applications of RL involve multiple agents, eg, playing chess and Go games, autonomous driving, and robotics. Unfortunately, the …
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
    • G06N3/12Computer systems based on biological models using genetic models
    • G06N3/126Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • 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
    • 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
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/04Inference methods or devices
    • G06N5/043Distributed expert systems, blackboards
    • 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
    • G06N3/00Computer systems based on biological models
    • G06N3/004Artificial life, i.e. computers simulating life
    • G06N3/006Artificial life, i.e. computers simulating life based on simulated virtual individual or collective life forms, e.g. single "avatar", social simulations, virtual worlds
    • 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
    • G06N7/00Computer systems based on specific mathematical models
    • G06N7/02Computer systems based on specific mathematical models using fuzzy logic
    • 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
    • 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

Similar Documents

Publication Publication Date Title
Ozdaglar et al. Independent learning in stochastic games
Bloembergen et al. Evolutionary dynamics of multi-agent learning: A survey
Er et al. Online tuning of fuzzy inference systems using dynamic fuzzy Q-learning
US8112369B2 (en) Methods and systems of adaptive coalition of cognitive agents
Tizhoosh Opposition-based learning: a new scheme for machine intelligence
García et al. No strategy can win in the repeated prisoner's dilemma: linking game theory and computer simulations
Carmel et al. Model-based learning of interaction strategies in multi-agent systems
van Eck et al. Application of reinforcement learning to the game of Othello
Wang et al. On the convergence of the monte carlo exploring starts algorithm for reinforcement learning
Sutton et al. The Alberta plan for AI research
Gummadi et al. Mean field analysis of multi-armed bandit games
Hafez et al. Topological Q-learning with internally guided exploration for mobile robot navigation
Subramanian et al. Multi-agent advisor Q-learning
Shah et al. On reinforcement learning for turn-based zero-sum Markov games
Mishra et al. Model-free reinforcement learning for stochastic Stackelberg security games
Shi et al. Efficient hierarchical policy network with fuzzy rules
Amhraoui et al. Expected Lenient Q-learning: a fast variant of the Lenient Q-learning algorithm for cooperative stochastic Markov games
Zhang et al. Opinion dynamics in gossiper-media networks based on multiagent reinforcement learning
Dockhorn Prediction-based search for autonomous game-playing
Tuyls et al. Multiagent learning
Ammar et al. Multi-agent architecture for Multi‐objective optimization of Flexible Neural Tree
Dahl The lagging anchor algorithm: Reinforcement learning in two-player zero-sum games with imperfect information
Papini Safe policy optimization
Fan et al. Optimal evolution strategy for continuous strategy games on complex networks via reinforcement learning
Shinkawa et al. Bandit approach to conflict-free multi-agent Q-learning in view of photonic implementation
点击 这是indexloc提供的php浏览器服务,不要输入任何密码和下载