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Decision-Making in Dynamic Environments

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Decision-Making in Dynamic Environments

This course is part of Build AI Agents with Practical App Design Specialization

Instructor: LearnQuest Network

Included with

Learn more

3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

3 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

3 hours to complete

Flexible schedule

Learn at your own pace

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

8 assignments¹

AI Graded see disclaimer

Taught in English

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Build your subject-matter expertise

This course is part of the Build AI Agents with Practical App Design Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 3 modules in this course

This module immerses learners in the strategic world of multi-agent interactions, highlighting how intelligent agents collaborate and compete to solve complex problems. By mastering game theory principles, distributed training, and robust communication protocols, participants develop the expertise to deploy and scale AI agent solutions for dynamic, real-world environments. Learners build essential skills to design coordinated agent behaviors, optimize networked systems, and manage decentralized intelligence, positioning themselves to drive innovation in industries where collective decision-making delivers critical value.

Module details

Reinforcement learning empowers autonomous AI agents to optimize decisions in complex, changing environments. In this module, learners will develop foundational expertise in designing reward structures, implementing sequential learning methods, and tuning agent behaviors for impact. Through practical case studies and hands-on exercises, participants will master how to align agent incentives with organizational goals, leverage temporal difference learning for adaption, and engineer strategies that balance exploration with exploitation. Prepare to drive real-world innovation by building robust RL systems that respond intelligently to evolving business needs.

What's included

9 videos1 reading2 assignments

9 videosTotal 16 minutes

Welcome to Decision-Making in Dynamic Environments3 minutes
Reinforcement Learning Fundamentals2 minutes
Design custom reward shaping functions2 minutes
Apply domain knowledge to craft high-impact reward signals2 minutes
Validate reward functions with meta-learning evaluation1 minute
Implement TD learning for real-time agent adaptation2 minutes
Optimize agent policies with Q-learning and Monte Carlo methods2 minutes
Balance exploration and exploitation to maximize cumulative rewards1 minute
From Fundamentals to Interactions2 minutes

1 readingTotal 5 minutes

Action Story: When Reward Design Backfires5 minutes

2 assignmentsTotal 36 minutes

Reinforcement Learning Fundamentals26 minutes
Design custom reward shaping functions for targeted agent outcomes10 minutes

What's included

7 videos1 reading3 assignments

7 videosTotal 9 minutes

Multi-Agent Interactions2 minutes
Model multi-agent interactions2 minutes
Engineer efficient information sharing for collaborative tasks1 minute
Build competitive agent strategies to dominate market simulations1 minute
Scale agent training1 minute
Implement peer-to-peer communication protocols for agent teams1 minute
Manage data consistency across decentralized agent networks1 minute

1 readingTotal 5 minutes

Action Story: When Collaboration Turns into Competition5 minutes

3 assignmentsTotal 42 minutes

Multi-Agent Interactions26 minutes
Model multi-agent interactions using Nash equilibrium concepts10 minutes
Scale agent training with distributed computing frameworks6 minutes

This module prepares learners to build agents that thrive in the constantly evolving, complex realities of business and society. By mastering adaptation to data and environment changes, enforcing fairness in decision processes, and designing defensively against adversarial threats, participants will develop the expertise to deploy resilient, ethical AI solutions. Learners acquire powerful tools and evidence-based strategies that enable robust agent performance in unpredictable markets, mission-critical environments, and diverse global contexts.