As we just saw, a basic value iteration approach can be used to update the Bellman equation and iteratively find ideal state-action pairs to optimally navigate a given environment. This approach actually stores new information at each time step, iteratively making our algorithm more intelligent. However, there is a problem with this method as well. It's simply not scalable! The taxi cab environment is simple enough, with 500 states and 6 actions, to be solved by iteratively updating the Q-values, thereby estimating the value of each individual state-action pair. However, more complex simulations, like a video game, may potentially have millions of states and hundreds of actions, which is why computing the quality of each state-action pair becomes computationally unfeasible and logically inefficient. The only option we are left with, in such circumstances...
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You're reading from Hands-On Neural Networks with Keras
Niloy Purkait is a technology and strategy consultant by profession. He currently resides in the Netherlands, where he offers his consulting services to local and international companies alike. He specializes in integrated solutions involving artificial intelligence, and takes pride in navigating his clients through dynamic and disruptive business environments. He has a masters in Strategic Management from Tilburg University, and a full specialization in data science from Michigan University. He has advanced industry grade certifications from IBM, in subjects like signal processing, cloud computing, machine and deep learning. He is also perusing advanced academic degrees in several related fields, and is a self-proclaimed lifelong learner.
Read more about Niloy Purkait
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Niloy Purkait is a technology and strategy consultant by profession. He currently resides in the Netherlands, where he offers his consulting services to local and international companies alike. He specializes in integrated solutions involving artificial intelligence, and takes pride in navigating his clients through dynamic and disruptive business environments. He has a masters in Strategic Management from Tilburg University, and a full specialization in data science from Michigan University. He has advanced industry grade certifications from IBM, in subjects like signal processing, cloud computing, machine and deep learning. He is also perusing advanced academic degrees in several related fields, and is a self-proclaimed lifelong learner.
Read more about Niloy Purkait