Optimal Control by Reinforcement Learning
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This is a description of work done on solution of Optimal Control problem by RL
Optimal Control Problem by RL (Reinforcement Learning) is a technique used to solve complex control problems in various industries such as robotics, transportation, and energy management. The aim of this technique is to maximize the performance of a system while minimizing the cost or risk involved.
RL algorithms learn from experience and feedback by interacting with the environment. They use trial and error methods to find the optimal solution for a particular control problem. The process involves defining a set of states, actions, rewards, and policies that govern the behavior of the system under control.
One major advantage of using RL for optimal control problems is its ability to handle complex and uncertain environments. It can adapt to changes in the environment or system dynamics without requiring prior knowledge about them.