First, we need to estimate the value (V) of state (s) while following a specific policy (π). This tells you the expected cumulative reward at the terminal state of a game while following a policy (π) starting at state (s). Why is this useful? Well, imagine that our learning agent's environment is populated by enemies that are continuously chasing the agent. It may have developed a policy dictating it to never stop running during the whole game. In this case, the agent should have enough flexibility to evaluate the value of game states (when it runs up to the edge of a cliff, for example, so as to not run off it and die). We can do this by defining the value function at a given state, V π (s), as the expected cumulative (discounted) reward that the agent receives from following that policy, starting from the current state:
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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.
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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