オンライン学習 Protected: Online convex optimization (3) exp concavity and ONS
Convex optimization for online prediction for digital transformation , artificial intelligence , and machine learning tasks (the case of exp concavity and ONS).
微分積分:Calculus
オンライン学習 オンライン学習 Protected: Online Convex Optimization (2) Complementing FTL Strategies with Regularization
Complementing the FTL strategy by introducing regularization techniques (L2 norm) in online prediction for digital transformation , artificial intelligence , and machine learning tasks.
オンライン学習 Protected: Online Convex Optimization(1) FTL strategy and BTL supplement
Online Convex Optimization and FTL Strategies with Online Prediction for Digital Transformation , Artificial Intelligence , and Machine Learning Tasks with BTL Supplement
オンライン学習 Protected: New Developments in Reinforcement Learning (2) – Approaches Using Deep Learning
On seven methods for improving deep reinforcement learning used in digital transformation , artificial intelligence , and machine learning tasks (first generation DQN, dual Q learning (dual DQN method), prioritized experience replay, collision Q networks, distributed reinforcement learning (categorical DQN method) noise networks, n-step cutting returns) and alpha zero
オンライン学習 Protected: New Developments in Reinforcement Learning (1) – Reinforcement Learning with Risk Indicators
Different approaches (regular process TD learning, RDPS methods) and implementations (Monte Carlo, analytical methods) in risk-aware reinforcement learning methods for digital transformation , artificial intelligence , and machine learning tasks.
オンライン学習 Protected: Partially Observed Markov Decision Processes (2) Planning POMDPs
Reinforcement learning for digital transformation , artificial intelligence , and machine learning tasks; obtaining optimal strategies using partial observation Markov decision process planning methods.
オンライン学習 Protected: Partially Observed Markov Decision Processes (1) On POMDPs and Belief MDPs
Belief MDPs, more flexible reinforcement learning using partially observed Markov decision processes (POMDPs) for digital transformation , artificial intelligence , and machine learning tasks.
オンライン学習 Protected: Reinforcement Learning with Function Approximation (3) – Function Approximation for Policy Functions
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オンライン学習 Protected: Reinforcement Learning with Function Approximation (2) – Function Approximation of Value Functions (For Online Learning)
Theory of function approximation online methods gradient TD learning, least-squares based least-squares TD learning (LSTD), GTD2)for reinforcement learning with a huge number of states used in digital transformation , artificial intelligence , and machine learning tasks, and regularization with LASSO.
強化学習 Protected: Reinforcement Learning with Function Approximation (1) – Function Approximation of Value Functions (Batch Learning Case)
Function approximation in the case of batch learning of value functions to deal with a huge number of states in reinforcement learning for digital transformation, artificial intelligence, and machine learning tasks.