Core

• `EtaCtrl`: Central controller for managing optimization workflows, including learning and execution processes.

Configuration

• `Config`: Represents the configuration of an experiment.

• `ConfigPaths`: Relative paths and file names of the run files (part of Config, configurable in the paths section of the experiment config file).

• `ConfigSetup`: Import and setup parameters for agent, environment, vectorizer, and policy (part of Config, configurable in the setup section of the experiment config file).

• `ConfigSettings`: Settings parameters such as sampling time, prediction horizon, and agent/environment/scenario settings (part of Config, configurable in the settings section of the experiment config file).

• `RunInfo`: Handles metadata and absolute paths of a single optimization run (Is derived by ETA Ctrl for each run).

Agent

• `MpcAgent`: Pyomo based optimization agent supporting multiple solvers.

• `RuleBased`: Simple agent using a control rule function.

Environment

• Base Classes:

  • `BaseEnv`: Abstract base class for creating custom environments.

  • `LiveEnv`: Extends BaseEnv for live environments interacting with real-world systems.

  • `PyomoSimEnv`: Extends BaseEnv for environments using a pyomo model for simulation.

  • `SimEnv`: Extends BaseEnv for environments using FMU-based simulations.

• Vectorization:

  • `NoVecEnv`: Custom vectorizer for environments that handle multithreading internally.

Simulation

• `FMUSimulator`: Provides functionality for simulating FMUs (Functional Mock-up Units).

Time Series

• `scenario_from_csv`: Imports and processes scenario data from CSV files.

• `df_from_csv`: Reads time series data from a CSV file and returns it as a pandas DataFrame.

• `df_resample`: Resamples the time index of a DataFrame to a specified frequency.

• `df_interpolate`: Interpolates missing values in a DataFrame with a specified frequency.

State Management

• `StateVar`: Represents a single variable in the state of an environment.

• `StateConfig`: Configures the action and observation spaces based on StateVar instances.