15 Topics
10 Days
74 Sessions
Programme Overview
Training Description
Who Should Attend
- Operations and supply chain managers
- Strategy and business transformation professionals
- Financial analysts and investment professionals
- Marketing analytics professionals
- Product managers
- Operations research professionals
- Technology and innovation managers
- Risk management professionals
- Decision science professionals
- Managers seeking to apply AI to business optimization
- Professionals involved in digital transformation and intelligent automation
Session Objectives
- Understand the fundamentals of reinforcement learning for practical applications.
- Master deep Q-networks (DQNs) for value-based RL.
- Utilize policy gradient methods for continuous control tasks.
- Implement multi-agent reinforcement learning (MARL).
- Design and build RL models for robotics applications.
- Optimize RL agents for game AI development.
- Troubleshoot and address complex RL implementation challenges.
- Implement model evaluation and validation techniques for RL.
- Integrate RL models into real-world systems.
- Understand how to design reward functions for effective learning.
- Explore advanced RL techniques (e.g., actor-critic methods, inverse RL).
- Apply real world use cases for RL in various domains.
- Leverage RL libraries for efficient model implementation.
About the Course
This advanced training introduces professionals to the practical application of reinforcement learning (RL) for solving complex business optimization and decision-making problems. Participants will explore how intelligent systems can learn from interactions, evaluate outcomes, and continuously improve decisions in dynamic business environments.The course focuses on practical applications such as pricing optimization, inventory management, supply chain decisions, resource allocation, customer engagement, marketing optimization, portfolio strategies, workforce scheduling, and operational planning. Participants will learn how to formulate business problems as reinforcement learning environments, select appropriate algorithms, evaluate model performance, and translate AI-driven recommendations into actionable business strategies.
Curriculum & Topics
15 Topics | 74 Sessions
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Workshop 1.1:
Fundamentals of reinforcement learning for practical applications.
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Workshop 1.2:
Overview of DQNs, policy gradients, and MARL.
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Workshop 1.3:
Setting up an RL development environment.
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Workshop 1.4:
Introduction to RL libraries and tools.
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Workshop 1.5:
Best practices for practical RL.
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Workshop 2.1:
Implementing DQNs for discrete action spaces.
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Workshop 2.2:
Utilizing experience replay and target networks.
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Workshop 2.3:
Designing and building DQN agents for various tasks.
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Workshop 2.4:
Optimizing DQN models for performance.
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Workshop 2.5:
Best practices for DQNs.
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Workshop 3.1:
Implementing policy gradient methods for continuous control.
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Workshop 3.2:
Utilizing REINFORCE and actor-critic algorithms.
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Workshop 3.3:
Designing and building policy gradient agents.
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Workshop 3.4:
Optimizing policy gradient models for complex tasks.
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Workshop 3.5:
Best practices for policy gradients.
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Workshop 4.1:
Implementing MARL for cooperative and competitive tasks.
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Workshop 4.2:
Utilizing centralized training and decentralized execution.
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Workshop 4.3:
Designing and building MARL environments.
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Workshop 4.4:
Optimizing MARL models for multi-agent scenarios.
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Workshop 4.5:
Best practices for MARL.
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Workshop 5.1:
Designing RL models for robotic control.
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Workshop 5.2:
Implementing RL for navigation and manipulation tasks.
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Workshop 5.3:
Utilizing simulation environments for robotic RL.
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Workshop 5.4:
Optimizing RL agents for real-world robotic systems.
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Workshop 5.5:
Best practices for robotics RL.
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Workshop 6.1:
Implementing RL for game playing and strategy.
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Workshop 6.2:
Utilizing RL for non-player character (NPC) behavior.
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Workshop 6.3:
Designing and building RL agents for game environments.
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Workshop 6.4:
Optimizing RL models for game AI performance.
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Workshop 6.5:
Best practices for game AI RL.
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Workshop 7.1:
Debugging complex RL implementation issues.
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Workshop 7.2:
Analyzing model performance and stability.
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Workshop 7.3:
Utilizing troubleshooting techniques for model improvement.
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Workshop 7.4:
Resolving common RL challenges.
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Workshop 7.5:
Best practices for troubleshooting.
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Workshop 8.1:
Implementing evaluation metrics for RL tasks.
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Workshop 8.2:
Utilizing simulation and real-world testing.
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Workshop 8.3:
Designing and building model validation pipelines.
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Workshop 8.4:
Optimizing model evaluation strategies.
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Workshop 8.5:
Best practices for model evaluation.
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Workshop 9.1:
Integrating RL models into real-world applications.
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Workshop 9.2:
Utilizing APIs and deployment tools for RL.
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Workshop 9.3:
Implementing real-time RL systems.
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Workshop 9.4:
Optimizing models for deployment environments.
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Workshop 9.5:
Best practices for integration.
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Workshop 10.1:
Designing effective reward functions for RL tasks.
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Workshop 10.2:
Utilizing shaping and sparse rewards.
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Workshop 10.3:
Designing and building reward engineering pipelines.
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Workshop 10.4:
Optimizing reward functions for learning efficiency.
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Workshop 10.5:
Best practices for reward design.
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Workshop 11.1:
Implementing actor-critic methods for continuous control.
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Workshop 11.2:
Utilizing inverse reinforcement learning (IRL).
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Workshop 11.3:
Designing and building advanced RL architectures.
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Workshop 11.4:
Optimizing advanced techniques for specific tasks.
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Workshop 11.5:
Best practices for advanced techniques.
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Workshop 12.1:
Implementing RL for autonomous driving.
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Workshop 12.2:
Utilizing RL for resource management and optimization.
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Workshop 12.3:
Implementing RL for personalized recommendation systems.
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Workshop 12.4:
Utilizing RL for financial trading strategies.
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Workshop 12.5:
Best practices for real-world applications.
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Workshop 13.1:
Utilizing OpenAI Gym for RL environments.
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Workshop 13.2:
Implementing RL algorithms with TensorFlow and PyTorch.
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Workshop 13.3:
Designing and building RL pipelines with libraries.
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Workshop 13.4:
Optimizing library usage for efficient implementation.
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Workshop 13.5:
Best practices for library implementation.
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Workshop 14.1:
Implementing model interpretability techniques for RL.
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Workshop 14.2:
Utilizing visualization tools for agent behavior.
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Workshop 14.3:
Designing and building interpretable RL models.
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Workshop 14.4:
Optimizing model transparency.
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Workshop 14.5:
Best practices for model interpretability.
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Workshop 15.1:
Emerging trends in reinforcement learning.
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Workshop 15.2:
Utilizing meta-learning for RL.
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Workshop 15.3:
Implementing distributed RL for large-scale systems.
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Workshop 15.4:
Best practices for future RL.
