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2024-12-30 01:06 本頁面
【正文】 e assign two arrows to each room of the previous graph. Each arrow contains an instant reward value. The graph bees state diagram as shown below Additional loop with highest reward (100) is given to the goal room (F back to F) so that if the agent arrives at the goal, it will remain there forever. This type of goal is called absorbing goal because when it reaches the goal state, it will stay in the goal state. Ladies and gentlemen, now is the time to introduce our superstar agent…. Imagine our agent as a dumb virtual robot that can learn through experience. The agent can pass one room to another but has no knowledge of the environment. It does not know which sequence of doors the agent must pass to go outside the building. Suppose we want to model some kind of simple evacuation of an agent from any room in the building. Now suppose we have an agent in Room C and we want the agent to learn to reach outside the house (F). (see diagram below) 3 How to make our agent learn from experience? Before we discuss about how the agent will learn (using Q learning) in the next section, let us discuss about some terminologies of state and action . We call each room (including outside the building) as a state . Agent39。s movement from one room to another room is called action . Let us draw back our state diagram. State is depicted using node in the state diagram, while action is represented by the arrow. Suppose now the agent is in state C. From state C, the agent can go to state D because the state C is connected to D. From state C, however, the agent cannot directly go to state B because there is no direct door connecting room B and C (thus, no arrow). From state D, the agent can go either to state
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