![]() How to handle the temporal limitation problem.What are the best strategies to use with DQL?.Thanks to this model, we’ll be able to create an agent that learns to play Doom! Our DQN Agent Instead of using a Q-table, we’ll implement a Neural Network that takes a state and approximates Q-values for each action based on that state. Today, we’ll create a Deep Q Neural Network. For more information and more resources, check out the syllabus of the course. This article is the third part of a series of blog post about Deep Reinforcement Learning. Last time, we learned about Q-Learning: an algorithm which produces a Q-table that an agent uses to find the best action to take given a state.īut as we’ll see, producing and updating a Q-table can become ineffective in big state space environments. ![]() By Thomas Simonini An introduction to Deep Q-Learning: let’s play Doom This article is part of Deep Reinforcement Learning Course with Tensorflow ?️. ![]()
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