Discrete-time rewards efficiently guide the extraction of continuous-time optimal control policy from system data

The concept of reward is central in reinforcement learning and is also widely used in the natural sciences, engineering, and social sciences. Organisms learn behavior by interacting with their environment and observing the resulting rewarding stimuli. The expression of rewards largely represents the perception of the system and defines the behavioral state of the dynamic system. In reinforcement learning, finding rewards that explain behavioral decisions of dynamic systems has been an open challenge.

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