Reinforcement Learning work

Reinforcement Learning (RL) system basically consist of two major components:


the environment refers to the setting that the agent is behaving on
and the agent represents the RL algorithm.

The RL process begins when the environment
sends a state to the agent,
which then depending on its observations,
takes an action in reaction to that state.

After that, the environment sends the next state
and the respective reward back to the agent.

However, the agent will update its knowledge with
the reward came back by the environment to evaluate its last action.

This loop continues until the environment sends
a terminal state that means when the agent has achieved all his tasks