Читать книгу Artificial Intelligent Techniques for Wireless Communication and Networking - Группа авторов - Страница 17

In the era of successful reinforcement learning, growing a deep reinforcement learning agent with allied tasks within a jointly learned representation would substantially increase sample academic success.

This is accomplished by causing genuine several pseudo-reward functions, such as immediate prediction of rewards (= 0), predicting pixel changes in the next measurement, or forecasting activation of some secret unit of the neural network of the agent.

The point is that learning similar tasks creates an inductive bias that causes a model to construct functions useful for the variety of tasks in the neural network. This formation of more essential characteristics, therefore, contributes to less over fitting. In deep RL, an abstract state can be constructed in such a way that it provides sufficient information to match the internal meaningful dynamics concurrently, as well as to estimate the estimated return of an optimal strategy. The CRAR agent shows how a lesser version of the task can be studied by explicitly observing both the design and prototype components via the description of the state, along with an estimated maximization penalty for entropy. In contrast, this approach would allow a model-free and model-based combination to be used directly, with preparation happening in a narrower conditional state space.