Terraria is a mechanically simple 2D sandbox game with a non-complex interface. As is the case with many sandbox games, the objective of the game is what the player decides it to be. However, the game contains many implicit objectives, such as staying alive or acquiring items. These implicit objectives are easy to specify due to the mechanical simplicity of the game, which makes the game a great environment for a reinforcement learning agent.
TerrarAI provides many key capabilities that a reinforcement learning environment requires. Among them, the TerrarAI provides:
- the reporting of internal game state (defined the player state, world state, and non-player character state)
- the configuration of the internal game state
- the automation of entering and exiting the game environment
These capabilities support key reinforcement learning functionality by providing methods to:
- obtain rewards and punishments for agents, and provide ways to judge when a training step should be concluded
- initialize game state and interact with agents during training, via dynamically or statically generated game state configurations
- fully automate the training process without the necessity to write scripts to interact with the game's UI
Before diving too deep, you might want to read Why Terraria Isn't the Best RL Environment
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