# Functions
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NewGame creates a new Hangman game for the given word.
NewSimpleAgent creates a SimpleAgent with the provided learning rate and discount factor.
NewStateAction creates a new StateAction for a State and Action.
Next uses an Agent and State to find the highest scored Action.
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# Structs
Choice implements Action for a character choice in a game of Hangman.
Game represents the state of any given game of Hangman.
SimpleAgent is an Agent implementation that stores Q-values in a map of maps.
StateAction is a struct grouping an action to a given State.
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# Interfaces
Action is an interface wrapping an action that can be applied to the model's current state.
Agent is an interface for a model's agent and is able to learn from actions and return the current Q-value of an action at a given state.
Rewarder is an interface wrapping the ability to provide a reward for the execution of an action in a given state.
State is an interface wrapping the current state of the model.