Page History: Reinforcement learning with functional representation
Compare Page Revisions
Page Revision: 2014/02/13 00:47
In reinforcement learning, an intelligent and autonomous agent is put in an environment. 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 real-world situations, such as driving vehicles and manipulating robotic hands, the mapping between the states to actions is commonly highly complex and hard to be linear. We expect that the agent can adaptively learn a policy to fit the complex situations. Functional representation, by which a function is represented as a combination of basis functions, is a powerful tool for learning non-linear functions.