Combining Q-Learning with Artificial Neural Networks in an Adaptive Light Seeking Robot
نویسندگان
چکیده
Q-learning is a reinforcement learning technique that works by learning an action-value function that gives the expected utility of performing a given action in a given state and following a fixed policy thereafter. The basic implementation uses a q-table to store the data. With increasing complexity in the environment and the agent, this approach fails to scale well as the space requirements become prohibitive. In this paper, we investigate an alternative implementation in which we use an artificial neural network as a function approximator and eliminate the need for an explicit table.
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