Function approximation - ppt download

The state space problem in model-free RL Number of states quickly becomes too large Even for trivial applications Learning becomes too dependent on right choice of exploration parameters Explore-exploit tradeoffs become harder to solve State space = 765 unique states
They work well for specific applications. But not as general models of learning to be in the world. Much remains to be learned about. Internal representations. Processes controlling internal representations. Embodied priors. How embodied priors interact with processes controlling internal representations. We will start talking about how this is currently being done computationally beginning next class. No class Feb 1.

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Function approximation - ppt download

Function approximation - ppt download

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Value Function Approximation via Low-Rank Models

Function Approximation - ppt video online download

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Value Function Approximation via Low-Rank Models

Value Function Approximation, Reinforcement Learning (INF8953DE), Lecture - 6