Page History: Reinforcement learning with functional representation
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Page Revision: 2014/02/13 00:41
In reinforcement learning, an intelligent and autonomous agent is put in an environement. It observes its state of the environment and take actions. After taking every action, the agent receives an reward from the environment and meanwhile changes its state. The aim of the agent is to learn a state-to-action mapping from the experience of its state-action-reward history, so that its accumulated long-term reward is maximized. The state-to-action mapping is usually called as a
policy.
In practice, we commonly expect that the policy can be a nonlinear mapping from the state features to the candidate actions, and thus has the ability to fit complex decision situations.
Functional representation, by which a function is represented as a combination of basis functions, is a powerful tool for learning non-linear functions