By doing so we ensure that our model will be able to generalize in the future under different circumstances. Once the agent becomes more proficient as solving a given task we need to slowly transition the mode of learning to Reinforcement Learning. The trick is to shift system’s attention on the demonstration in the beginning to a higher degree. With ML Agents we can easily enable the Imitation Learning through the configuration file. Furthermore, we can combine this technique with the Reinforcement Learning method to get even better results. The system will try to mimic moves from previously recorded session to learn faster. The human supervision over actions sequence means that we have a better control over what neural network actually learns. Instead of letting our AI to randomly select actions we can demonstrate, which ones it should be using. This is where the concept of Imitation Learning comes in. In addition, they need to occur in a specific order at the correct time! The concept of Imitation Learning All of a sudden we have a bunch of new activities that agent needs to carry out during a single episode. Now imagine a situation in which the AI needs to perform a specific sequence of actions in order to reach its goal. The agent has to select a single action of moving either left or right to find a treasure. The problem that we have given our enemy AI last time was relatively simple. That inevitably may lead not only to the extension of the training sessions but also the danger of less performant model. Secondly, it may take a long time for the agent to determine the right combination of actions to solve a task at hand. First, the agent randomly tries to apply different actions before it can receive a reward or penalty. There are two observations that we can make about this type of the training. The session results in an AI model that we can later use in the game. The Reinforcement Learning is based on teaching our agent how to solve a given task over time by rewarding or penalizing it for its actions. Last time we have built a solid foundation of implementing Deep Learning concept in our games. The limitations of Reinforcement Learning
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