Action: For every state, an agent needs to take an action toward achieving its goal.- Agent: The piece of software you are training is called an agent. It makes decisions in an environment to reach a goal.
- Discriminator: A neural network trained to differentiate between real and synthetic data.
- Discriminator loss: Evaluates how well the discriminator differentiates between real and fake data.
- Edit event: When a note is either added or removed from your input track during inference.
- Environment: The environment is the surrounding area within which the agent interacts.
Exploration versus exploitation: An agent should exploit known information from previous experiences to achieve higher cumulative rewards, but it also needs to explore to gain new experiences that can be used in choosing the best actions in the future.
Generator: A neural network that learns to create new data resembling the source data on which it was trained.
- Generator loss: Measures how far the output data deviates from the real data present in the training dataset.
- Hidden layer: A layer that occurs between the output and input layers. Hidden layers are tailored to a specific task.
- Input layer: The first layer in a neural network. This layer receives all data that passes through the neural network.
- Output layer: The last layer in a neural network. This layer is where the predictions are generated based on the information captured in the hidden layers.
- Piano roll: A two-dimensional piano roll matrix that represents input tracks. Time is on the horizontal axis and pitch is on the vertical axis.
- Reward: Feedback is given to an agent for each action it takes in a given state. This feedback is a numerical reward.
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