Strategy

Protected: Value Assessment and Policy and Weaknesses in Deep Reinforcement Learning

Value assessment and strategies and weaknesses in deep reinforcement learning used for digital transformation, artificial intelligence, and machine learning tasks poor sample efficiency, difficulty in validating methods as well, impact of implementation practices on performance, library initial values, poor reproducibility, over-training, local optimum, dexterity, TRPO, PPO, continuous value control, image control, policy-based, value-based

2023.03.30

アルゴリズム:Algorithmsグラフ理論スパースモデリング幾何学:Geometry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: Applying Neural Networks to Reinforcement Learning Applying Deep Learning to Strategy:Advanced Actor Critic (A2C)

Application of Neural Networks to Reinforcement Learning for Digital Transformation, Artificial Intelligence, and Machine Learning tasks Implementation of Advanced Actor Critic (A2C) applying deep learning to strategies (Policy Gradient method, Q-learning, Gumbel Max Trix, A3C (Asynchronous Advantage Actor Critic))

2023.03.02

アルゴリズム:Algorithmsグラフ理論スパースモデリング幾何学:Geometry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: Application of Neural Networks to Reinforcement Learning Policy Gradient, which implements a strategy with a function with parameters.

Application of Neural Networks to Reinforcement Learning for Digital Transformation, Artificial Intelligence, and Machine Learning tasks Policy Gradient to implement strategies with parameterized functions (discounted present value, strategy update, tensorflow, and Keras, CartPole, ACER, Actor Critoc with Experience Replay, Off-Policy Actor Critic, behavior policy, Deterministic Policy Gradient, DPG, DDPG, and Experience Replay, Bellman Equation, policy gradient method, action history)

2023.02.16

アルゴリズム:Algorithmsグラフ理論スパースモデリングマルチエージェントシステム幾何学:Geometry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: the application of neural networks to reinforcement learning(1) overview

Overview of the application of neural networks to reinforcement learning utilized in digital transformation, artificial intelligence and machine learning tasks (Agent, Epsilon-Greedy method, Trainer, Observer, Logger, Stochastic Gradient Descent, Stochastic Gradient Descent, SGD, Adaptive Moment Estimation, Adam, Optimizer, Error Back Propagation Method, Backpropagation, Gradient, Activation Function Stochastic Gradient Descent, SGD, Adaptive Moment Estimation, Adam, Optimizer, Error Back Propagation, Backpropagation, Gradient, Activation Function, Batch Method, Value Function, Strategy)

2022.12.14

python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

Protected: Implementation of Model-Free Reinforcement Learning in python (3)Using experience for value assessment or strategy update: Value-based vs. policy-based

Value-based and policy-based implementations of model-free reinforcement learning in python for digital transformation, artificial intelligence, and machine learning tasks

2022.12.02

python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確率・統計:Probability and Statistics

Theory and Algorithms for the Bandit Problem

The theory and algorithms of the Bandit Problem for selecting optimal strategies to be utilized in digital transformation, artificial intelligence, and machine learning tasks

2022.08.11

バンディッド問題強化学習