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You are here		www.assemblyai.com Reinforcement Learning With (Deep) Q-Learning Explained
\|	\|	niklasriewald.com The math behind competitive Pokemon, Part 2: Value Function Approximation \| Niklas Riewald	10.4 parsecs away Travel
\|	\|	The last post was about playing optimal Pokemon games by calculating the value function V(x) using Bellmann equations. Unfortunately, this was impractical, so we need another way to calculate the likelihood of winning games. One solution to this problem is called Value Function Approximation. The idea behind it is to not calculate the value function...	10.4 parsecs away Travel
\|	\|	www.exxactcorp.com What You Need to Know About Deep Reinforcement Learning \| Exxact Blog	8.2 parsecs away Travel
\|	\|	Exxact	8.2 parsecs away Travel
\|	\|	analytixon.com If you did not already know \| AnalytiXon	15.9 parsecs away Travel
\|	\|	Q-Learning Sine-Cosine Algorithm (QLSCA) The sine-cosine algorithm (SCA) is a new population-based meta-heuristic algorithm. In addition to exploiting sine and cosine functions to perform local and...	15.9 parsecs away Travel
\|	\|	polukhin.tech Lightweight Neural Network Architectures \| Andrii Polukhin	69.9 parsecs away Travel
\|		As the field of Deep Learning continues to grow, the demand for efficient and lightweight neural networks becomes increasingly important. In this blog post, we will explore six lightweight neural network architectures.	69.9 parsecs away Travel