Mastering Deep Q-Learning with GYM-Cliff Walking Environment
From Theory to Practice: Building Intelligent Agents with Deep Q-Learning in the "CliffWalking" Environment.
4.75 (2 reviews)
33
students
3.5 hours
content
Nov 2024
last update
$13.99
regular price
What you will learn
Bellman Equation: Understand the foundational equation that drives intelligent decision-making in reinforcement learning.
"gym" and "deque": Master the usage of essential tools to implement Deep Q-Learning algorithms efficiently.
Deep Learning Integration: Discover how to combine Deep Learning techniques with Q-Learning to enhance agent performance.
"GYM-CliffWalking" Environment: Gain hands-on experience navigating a challenging environment to optimize agent behavior.
Optimal Decision-Making: Develop strategies for making intelligent choices in dynamic and complex scenarios.
Practical Examples: Explore real-world applications and case studies to see Deep Q-Learning in action.
Implementation Best Practices: Learn tips and tricks for efficient algorithm implementation and performance optimization.
Intelligent Agent Design: Build agents capable of solving problems and adapting to changing environments.
Troubleshooting and Problem-Solving: Develop skills to overcome challenges and fine-tune agent performance.
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6037800
udemy ID
23/06/2024
course created date
15/07/2024
course indexed date
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