Reinforcement Learning

Protected: Implementation of model-free reinforcement learning in python (1) epsilon-greedy method

Implementation in python of the epsilon-Greedy method, a model-free reinforcement learning method for use in digital transformation, artificial intelligence, and machine learning tasks, multi-armed bandit

2022.11.03

アルゴリズム:Algorithmsマルチエージェントシステム幾何学:Geometry強化学習最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra集合論:Set theory

Protected: Overview of model-based approach to reinforcement learning and its implementation in python

Overview of reinforcement learning with model-based approaches used for digital transformation, artificial intelligence, and machine learning tasks and its implementation in python Bellman Equation, Value Iteration, Policy Iteration

2022.10.14

pythonアルゴリズム:Algorithmsオンライン学習強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Overviews of reinforcement learning and implementation of a simple MDP model

Overview of reinforcement learning used for digital transformation (DX), artificial intelligence (AI), and machine learning (ML) tasks and implementation of a simple MDP model in python

2022.09.30

アルゴリズム:Algorithms強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Overview and history of the banded problem and its relationship to reinforcement learning/online learning

Overview and history of bandit problems utilized in digital transformation, artificial intelligence, and machine learning tasks and their relationship to reinforcement learning online learning

2022.09.16

アルゴリズム:Algorithmsバンディッド問題強化学習機械学習:Machine Learning深層学習:Deep Learning

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.

2022.02.02

オンライン学習強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics

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.

2022.02.01

オンライン学習強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

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.

2022.01.28

オンライン学習強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics

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.

2022.01.26

強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Model-based reinforcement learning(Sparse sampling, UCT, Monte Carlo search tree)

Model-based reinforcement learning (sparse sampling, UCT, Monte Carlo search trees) used for digital transformation artificial intelligence , and machine learning tasks.

2022.01.24

IOT技術:IOT TechnologyStream Data Processingオンライン学習強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Model-free reinforcement learning(1) – Value iteration methods (Monte Carlo, TD, TD(λ))

Application of value iterative methods (Monte Carlo, TD, TD(λ)) to model-free reinforcement learning used in digital transformation , artificial intelligence , and machine learning.

2022.01.20

オンライン学習強化学習推論技術:inference Technology機械学習:Machine Learning