Evolutionary Algorithms vs Reinforcement Learning.

What is the difference between Reinforcement Learning and Evolutionary Algorithms? When should you use which? People often get confused in the differences between Artificial Intelligence Agents developed using Reinforcement Learning vs AI bots using Evolutionary Algorithms. Both protocols have a very similar rationale for working wherein our learning agents interact with our environment and learn from the feedback. There are some superficial differences between them when it comes to the technical details. But very few people really understand how these differences translate into different performances/benefits. This video will explain them to you. Understanding this distinction will help you improve your Machine Learning and Data Analysis pipelines. Evolutionary Algorithms are not based on gradient-based methods. This allows us to implement an evolutionary algorithm in a much greater variety of contexts. They are also relatively straightforward, which allows for easy understanding and logging. The process of starting with multiple candidate solutions allows for a greater ability to traverse the solution space for optimal solutions. Combine this with the mutation, and crossover, and EAs become simple but powerful solutions you can implement in many contexts. RL agents also provide a few distinct advantages. Given that we can transform our loss landscape into something differentiable, RL can be extremely powerful. Our agents will be able to fit much more complex data (something not always true with EAs). In cases where every fraction point of performance is crucial, and we have resources, RL might be the correct solution. Reinforcement Learning agents will also learn the good and bad behavior for a particular environment allowing for a greater degree of generalization. Building this requires a thorough understanding of domain handling, data handling, and ML protocols. Like the video (and follow) to learn more about these topics. Comment which topics you want to learn about next. Ways to support my content are in the YT video description. This is part of my Machine Learning Techniques Playlist. Machine Learning Techniques focuses on how different research teams and problem solvers use various Machine Learning Tools and Techniques to solve various problems. Seeing their innovative approaches should show you the various ways we can combine and use AI and ML tricks and tools to break down very complex problems. It will interest you and hopefully inspire you. Video on Reinforcement Learning: https://www.youtube.com/watch?v=_J1Xn8fgUAc&t=73s Why you should implement Evolutionary Algorithms in your Machine Learning Projects: https://medium.com/mlearning-ai/why-you-should-implement-evolutionary-algorithms-in-your-machine-learning-projects-ee386edb4ecc Like Subscribe and Follow across other social media to learn more. For monetary support, check out the links below. Any amount helps a lot and is greatly appreciated. Donating provides lots of benefits like special sessions, annotated papers, and discounted consulting rates. Venmo: https://account.venmo.com/u/FNU-Devansh Paypal: paypal.me/ISeeThings Reach out to me: Check out my other articles on Medium. : https://machine-learning-made-simple.medium.com/ My YouTube: https://rb.gy/88iwdd Reach out to me on LinkedIn: https://www.linkedin.com/in/devansh-devansh-516004168/ My Instagram: https://rb.gy/gmvuy9 My Twitter: https://twitter.com/Machine01776819 Prepare for your Software Engineering Interviews: https://codinginterviewsmadesimple.substack.com/ Live conversations at twitch here: https://rb.gy/zlhk9y Get a free stock on Robinhood: https://join.robinhood.com/fnud75 #MachineLearning #Google #Evolution #Facebook #Devansh #MachineLearningMadeSimple #DeepLearning #Hustle #Algorithms #NeuralNetworks #ArtificialIntelligence #Python #RL