Udemy - Reinforcement Learning Foundations for Business

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size2.6 GB
  • Uploaded Byfreecoursewb
  • Downloads31
  • Last checkedJul. 03rd '26
  • Date uploadedJul. 02nd '26
  • Seeders 0
  • Leechers5

Infohash : 9D618E97147124E0490D55A4662F769CEABFF4A2

Reinforcement Learning Foundations for Business

https://WebToolTip.com

Published 6/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English + subtitle | Duration: 2h 59m | Size: 2.59 GB

Master reinforcement learning core concepts, Markov Decision Processes, Q-learning, DQN, and PPO with Python application

What you'll learn
Formulate business problems as Markov Decision Processes (MDP) using agents, environments, and rewards.
Differentiate between exploration and exploitation strategies using multi-armed bandit frameworks.
Evaluate and implement temporal-difference learning algorithms including SARSA and Q-learning.
Explain the operational necessity of function approximation and Deep Q-Networks (DQN) for complex states.
Analyze policy gradient methods, including REINFORCE, actor-critic architectures, and PPO.
Interpret fundamental Python and Gymnasium code structures for reinforcement learning agents.
Align enterprise Key Performance Indicators (KPIs) with optimal reward engineering design.
Assess the technical viability of reinforcement learning for enterprise use cases like warehouse routing.

Requirements
Basic proficiency in Python programming.
Fundamental understanding of probability and basic statistics.
No prior reinforcement learning or deep learning experience is required.

Files:

[ WebToolTip.com ] Udemy - Reinforcement Learning Foundations for Business
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1 - Course Framing and Orientation
    • 1 - Welcome and Scope (Description).html (1.7 KB)
    • 1 - Welcome and Scope.en_US.srt (15.0 KB)
    • 1 - Welcome and Scope.mp4 (98.8 MB)
    • 2 - Why RL, and the Running Case (Description).html (1.6 KB)
    • 2 - Why RL, and the Running Case.en_US.srt (10.6 KB)
    • 2 - Why RL, and the Running Case.mp4 (69.7 MB)
    10 - Function Approximation, Warehouse, and Wrap-Up
    • 20 - Function Approximation, Warehouse, and Wrap-Up (Description).html (1.7 KB)
    • 20 - Function Approximation, Warehouse, and Wrap-Up.en_US.srt (11.0 KB)
    • 20 - Function Approximation, Warehouse, and Wrap-Up.mp4 (97.2 MB)
    2 - Reinforcement Learning Foundations
    • 1 - Knowledge Check.html (21.6 KB)
    • 3 - The Agent-Environment Loop (Description).html (1.6 KB)
    • 3 - The Agent-Environment Loop.en_US.srt (15.0 KB)
    • 3 - The Agent-Environment Loop.mp4 (144.4 MB)
    • 4 - Rewards and the Explore-Exploit Tension (Description).html (1.6 KB)
    • 4 - Rewards and the Explore-Exploit Tension.en_US.srt (7.4 KB)
    • 4 - Rewards and the Explore-Exploit Tension.mp4 (56.0 MB)
    3 - Markov Decision Processes and Value Functions
    • 2 - Knowledge Check.html (26.2 KB)
    • 5 - The MDP Model (Description).html (1.6 KB)
    • 5 - The MDP Model.en_US.srt (17.2 KB)
    • 5 - The MDP Model.mp4 (181.7 MB)
    • 6 - Bellman Equations and Optimality (Description).html (1.6 KB)
    • 6 - Bellman Equations and Optimality.en_US.srt (21.3 KB)
    • 6 - Bellman Equations and Optimality.mp4 (213.3 MB)
    4 - Exploration and Multi-Armed Bandits
    • 3 - Knowledge Check.html (25.6 KB)
    • 7 - The Bandit Setting (Description).html (1.6 KB)
    • 7 - The Bandit Setting.en_US.srt (17.3 KB)
    • 7 - The Bandit Setting.mp4 (166.2 MB)
    • 8 - Comparing Exploration Strategies (Description).html (1.7 KB)
    • 8 - Comparing Exploration Strategies.en_US.srt (15.9 KB)
    • 8 - Comparing Exploration Strategies.mp4 (150.9 MB)
    5 - Temporal-Difference Learning SARSA and Q-Learning
    • 10 - SARSA and Q-Learning Control (Description).html (1.6 KB)
    • 10 - SARSA and Q-Learning Control.en_US.srt (17.2 KB)
    • 10 - SARSA and Q-Learning Control.mp4 (183.2 MB)
    • 11 - Tuning and Practical Training (Description).html (1.6 KB)
    • 11 - Tuning and Practical Training.en_US.srt (12.5 KB)
    • 11 - Tuning and Practical Training.mp4 (128.6 MB)
    • 4 - Knowledge Check.html (26.7 KB)
    • 9 - Temporal-Difference Prediction (Description).html (1.6 KB)
    • 9 - Temporal-Difference Prediction.en_US.srt (15.9 KB)
    • 9 - Temporal-Difference Prediction.mp4 (153.7 MB)
    6 - Function Approximation and Deep Q-Learning
    • 12 - From Tables to Features (Description).html (1.7 KB)
    • 12 - From Tables to Features.en_US.srt (13.7 KB)
    • 12 - From Tables to Features.mp4 (135.2 MB)
    • 13 - Deep Q-Networks (Description).html (1.6 KB)
    • 13 - Deep Q-Networks.en_US.srt (21.9 KB)
    • 13 - Deep Q-Networks.mp4 (207.5 MB)
    • 5 - Knowledge Check.html (26.7 KB)
    7 - Policy Gradients and REINFORCE
    • 14 - Optimizing the Policy Directly (Description).html (1.7 KB)
    • 14 - Optimizing the Policy Directly.en_US.srt (13.5 KB)
    • 14 - Optimizing the Policy Directly.mp4 (133.0 MB)
    • 15 - Variance, Baselines, and Relevance (Description).html (1.6 KB)
    • 15 - Variance, Baselines, and Relevance.en_US.srt (13.5 KB)
    • 15 - Variance, Baselines, and Relevance.mp4 (141.6 MB)
    • 6 - Knowledge Check.html (25.6 KB)
    8 - Actor-Critic and PPO
    • 16 - Actor-Critic Methods (Description).html (1.6 KB)
    • 16 - Actor-Critic Methods.en_US.srt (10.9 KB)
    • 16 - Actor-Critic Methods.mp4 (108.7 MB)
    • 17 - PPO and Choosing a Method (Description).html (1.7 KB)
    • 17 - PPO and Choosing a Method.en_US.srt (10.5 KB)
    • 17 - PPO and Choosing a Method.mp4 (98.3 MB)
    • 7 - Knowledge Check.html (25.4 KB)
    9 - Business Applications and the Warehouse Case
    • 18 - The Warehouse Case Study (Description).html (1.7 KB)
    • 18 - The Warehouse Case Study.en_US.srt (9.9 KB)
    • 18 - The Warehouse Case Study.mp4 (92.3 MB)
    • 19 - Mini-Cases and When RL Fits (Description).html (1.7 KB)
    • 19 - Mini-Cases and When RL Fits.en_US.srt (10.8 KB)
    • 19 - Mini-Cases and When RL Fits.mp4 (88.9 MB)
    • 8 - Knowledge Check.html (26.0 KB)
    • Bonus Resources.txt (0.1 KB)

Code:

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