Class Hierarchy

Go to the graphical class hierarchy

This inheritance list is sorted roughly, but not completely, alphabetically:

[detail level 123]

Application
mic::application::GridworldValueIteration	Class responsible for solving the gridworld problem by applying the reinforcement learning value iteration method
Importer
mic::importers::MazeMatrixImporter
mic::algorithms::MazeHistogramFilter	Class implementing a histogram filter based solution of the maze-of-digits localization problem
OpenGLApplication
mic::application::HistogramFilterMazeLocalization	Class implementing a histogram filter based solution of the maze-of-digits problem
mic::application::nArmedBanditsSofmax	Class implementing a n-Armed Bandits problem solving the n armed bandits problem using Softmax Action Selection
mic::application::nArmedBanditsUnlimitedHistory	Class implementing a n-Armed Bandits problem solving the n armed bandits problem based on unlimited history action selection (storing all action-value pairs)
mic::application::TestApp	Class implementing a n-Armed Bandits problem solving the n armed bandits problem using simple Q-learning rule
OpenGLEpisodicApplication
mic::application::EpisodicHistogramFilterMazeLocalization	Application for episodic testing of convergence of histogram filter based maze-of-digits localization
mic::application::GridworldDeepQLearning	Class responsible for solving the gridworld problem with Q-learning and (not that) deep neural networks
mic::application::GridworldDRLExperienceReplay	Class responsible for solving the gridworld problem with Q-learning, neural network used for approximation of the rewards and experience replay using for (batch) training of the neural network
mic::application::GridworldDRLExperienceReplayPOMDP	Class responsible for solving the gridworld problem with Q-learning, neural network used for approximation of the rewards and experience replay using for (batch) training of the neural network. In this case there is an assumption that the agent observes only part of the environment (POMPD)
mic::application::GridworldQLearning	Class responsible for solving the gridworld problem with Q-learning
mic::application::MazeOfDigitsDLRERPOMPD	Application of Partially Observable Deep Q-learning with Experience Reply to the maze of digits problem. There is an assumption that the agent observes only part of the environment (POMPD)
mic::application::MNISTDigitDLRERPOMDP	Application of Partially Observable Deep Q-learning with Experience Reply to the MNIST digits problem. There is an assumption that the agent observes only part of the environment - a patch of the whole image (POMPD)
PropertyTree
mic::environments::Environment	Abstract class representing an environment
mic::environments::Gridworld	Class emulating the gridworld environment
mic::environments::MazeOfDigits	Class emulating the maze of digits environment
mic::environments::MNISTDigit	Class emulating the MNISTDigit digit environment
mic::types::SpatialExperience	Structure storing a spatial experience - a triplet of position in time t, executed action and position in time t+1
SpatialExperienceBatch
mic::types::SpatialExperienceMemory	Class representing the spatial experience memory - used in memory replay. Derived from the Batch class