v0.2.0

What has changed:

• The learning performance has been improved
• The learning can now be also performed on CUDA enabled devices
• Added tracking of evaluation periods for better learning performance evaluation
• Storage units behavior bugs have been adressed
• Now several simulation can be started in parallel
• New Grafana dashboard definitions for easier analysis
• Docker compose file now includes Renderer to save plots directly from Grafan dashboards

v0.1.0 - initial release

This is the initial release of assume-framework, which is published to PyPi.

The assume-framework allows to define different energy market designs and includes reinforcement learning capabilities.

EIA-2023 - Market Abstraction Paper

This tag is used to release a citable version of the code used for the conference paper:
"Market Abstraction of Energy Markets and Policies – Application in an Agent-Based Modeling Toolbox"