Respuesta :
Answer:
No
Explanation:
Temporal difference or some times written as TD learning process may be defined as an approach to learning that describes how to predict a given quantity which depends on the future values for a given signal.
TD or temporal difference learning does not require the knowledge of transition probability tables. It only requires the knowledge of state and action plan. It also does not require the knowledge of reward function.
The agents does not need to acquire the transition model while using Temporal Difference Training. The transfer happens between regions, and the agent only changes the nations that are actually affected.
Only continuous action space information is required for Temporal Difference Learning.
Temporal-Difference does not require a model. Methods that use Temporal Differences learn directly through experience and contact with the environment.
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