The meaning of time in reinforcement learning

Reinforcement learning (RL) is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning is concerned with how software agents ought to take actions in an environment in order to maximize the notion of cumulative reward through the process of trial and error.

In reinforcement learning an agent starts at an empty state then analyzes the available datasets according to a policy of positive states and negative states. Rather than being explicitly taught as in supervised learning the correct set of actions for performing a task, reinforcement learning uses rewards as signals for positive states and punishments as signals for negative states.

The agent obtains the best path to a desirable reward as a cumulation of positive states and negative states. As compared to unsupervised learning, reinforcement learning is different in terms of goals. While the goal in unsupervised learning is to find similarities and differences between data points, in reinforcement learning the goal is to find a suitable action model that would maximize the total cumulative reward of the agent.

With the idea of reinforcement learning in mind I can explain to you the meaning of time. Time is very important in reinforcement learning since it describes the order of a sequence of positive states and negative states as well as the latency of positive states becoming negative states. More time or less time between states can signal undiscovered relationships in the environment which could point to more correct paths of obtaining a desirable reward.

But the problem with analyzing time between states is that the action of analyzing time is not equal to the action of recording time. Likewise relying on the data from the environment to provide a timestamp per state is not actually the real time of when the state occurred in the environment. Because in computing there are only two methods for precisely measuring time. There is manual code instrumentation known as data logging and automated code instrumentation known as data profiling.

So in reinforcement learning estimated times and real times and logged timestamps are ignored in favor of the order the agent ingests the data from the environment since the action of reinforcement learning happens after ingestion. And the action of reinforcement learning did not happen in the past during data logging or data profiling. The validity of this optimization is proven when the agent knowns all possible sequences of positive states and negative states from its environment whenever the environment ceases to provide new sequences.

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