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The Goals Are What the Agent Wants to Achieve

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The Goals Are What the Agent Wants to Achieve

First off, we have both the agent, on the left, and Communication Directors Email Lists its environment, on the right hand side. You can think of the environment as where the agent ‘lives’ and goes about its business of trying to achieve its goals.

The room is the environment for the Roomba. Your web app lives in an environment that is made up of your users, which is a much more complex and dynamic place than a room, but the general concept remains the same.

What are Goals and Rewards?

Communication Directors Email Lists

what it is striving to do. When the agent achieves a goal, it gets a reward based on the value of the goal. This is the same idea as positive reinforcement in learning theory. If, for example, the goal of the agent is to increase online sales, the reward might be the value of the sale, or the percentage of sessions that convert to a sale.

Given that the agent has a set of goals and allowable actions, the agent’s task is to learn what actions to take for each situation is finds itself in – so what it “sees,” “hears,” “feels,” etc.

Assuming the agent is trying to maximize the total value of its goals over time, then it needs to select the action that maximizes this value, for each different observation it can make.

To figure out how to best perform its task, the agent takes the following two basic steps::

  • Act First
    • Observe the environment to determine its current situation. You can think of this as data collection, so what we normally do in web analytics.
    • Predict the best next action to perform from the collection of allowable actions.
    • Take an action.
  • Then Learn from Action
    • Observe the environment again to see what was the impact of our action.
    • Evaluate how good or bad the impact was – did it lead to goal? If not, does it appear that we are closer or further away from reaching a goal than before we took the action?
    • Update the prediction model based on how much the action ‘moved’ the agent closer to or further from a goal.

By repeating this process, the agent learns which actions work best in each situation.

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