アルゴリズム:Algorithms Protected: Reinforcement learning application areas (2)
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強化学習
アルゴリズム:Algorithms アルゴリズム:Algorithms Protected: Reinforcement learning application areas (1)Behavior Optimization
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アルゴリズム:Algorithms Protected: Overcoming Weaknesses in Deep Reinforcement Learning Dealing with Locally Optimal Behavior/Overlearning (2) Inverse Reinforcement Learning
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アルゴリズム:Algorithms Protected: Overcoming Weaknesses in Deep Reinforcement Learning Dealing with Locally Optimal Behavior/Overlearning(1)Imitation Learning
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python Protected: Overcoming Weaknesses in Deep Reinforcement Learning Dealing with Poor Reproducibility: Evolutionary Strategies
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アルゴリズム:Algorithms Protected: Application of the Bandit Method (2) Internet Advertising
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アルゴリズム:Algorithms Protected: Applications of the Bandit Method (1) Monte Carlo Tree Search
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アルゴリズム:Algorithms Protected: Research Trends in Deep Reinforcement Learning: Meta-Learning and Transfer Learning, Intrinsic Motivation and Curriculum Learning
Research trends in deep reinforcement learning for digital transformation, artificial intelligence, and machine learning tasks: meta-learning and transfer learning, intrinsic motivation and curriculum learning automatic curriculum generation, automatic task decomposition, task difficulty adjustment, intrinsic reward, robot domain transformation, robot domain transformation, simulator to simulator transfer learning, BERT, Metric/Representation Base, Memory/Knowledge Base, active learning, meta-learning, and robot domain transformation) Robot domain transformation, transfer learning from simulators, BERT, Model-Agnostic Meta-Learning, Active Learning, Metric/Representation Base, Memory/Knowledge Base, Weigh Base, and Learning to Optimize
アルゴリズム:Algorithms Protected: Optimal arm bandit and Bayesian optimal when the player’s candidate actions are huge or continuous (2)
Bayesian optimization for digital transformation, artificial intelligence, machine learning tasks and bandit when player behavior is massive/continuous Markov chain Monte Carlo, Monte Carlo integration, turn kernels, scale parameters, Gaussian kernels, covariance function parameter estimation, Simultaneous Optimistic Optimazation policy, SOO strategy, algorithms, GP-UCB policy, Thompson's law, expected value improvement strategy, GP-UCB policy
アルゴリズム:Algorithms Protected: Implementation of two approaches to improve environmental awareness, a weak point of deep reinforcement learning.
Implementation of two approaches to improve environment awareness, a weakness of deep reinforcement learning used in digital transformation, artificial intelligence, and machine learning tasks (inverse predictive, constrained, representation learning, imitation learning, reconstruction, predictive, WorldModels, transition function, reward function Weaknesses of representation learning, VAE, Vision Model, RNN, Memory RNN, Monte Carlo methods, TD Search, Monte Carlo Tree Search, Model-based learning, Dyna, Deep Reinforcement Learning)