Tabular Q-learning
The key question to focus on when trying to handle the state space issue is, do we really need to iterate over every state in the state space? We have an environment that can be used as a source of real-life samples of states. If some state in the state space is not shown to us by the environment, why should we care about its value? We can only use states obtained from the environment to update the values of states, which can save us a lot of work.
This modification of the value iteration method is known as Q-learning, as mentioned earlier, and for cases with explicit state-to-value mappings, it entails the following steps:
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Start with an empty table, mapping states to values of actions.
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By interacting with the environment, obtain the tuple s, a, r, s′ (state, action, reward, and the new state). In this step, you need to decide which action to take, and there...
