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intelligent-environments-lab · Nov 5, 2024 · 17ab5b8 · 17ab5b8
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diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -24,7 +24,7 @@ CityLearn
 
 CityLearn is an open source Farama Foundation Gymnasium environment for the implementation of Multi-Agent Reinforcement Learning (RL) for building energy coordination and demand response in cities :cite:p:`https://doi.org/10.48550/arxiv.2012.10504, 10.1145/3360322.3360998, doi:10.1080/19401493.2024.2418813`. A major challenge for RL in demand response is the ability to compare algorithm performance :cite:p:`VAZQUEZCANTELI20191072`. Thus, CityLearn facilitates and standardizes the evaluation of RL agents such that different algorithms can be easily compared with each other.
 
-.. image:: ../../assets/images/environment.jpg
+.. image:: ../../assets/images/dr.jpg
    :scale: 30 %
    :alt: demand response
    :align: center

diff --git a/docs/source/overview/environment.rst b/docs/source/overview/environment.rst
@@ -4,8 +4,8 @@ Environment
 
 CityLearn includes energy models of buildings and distributed energy resources (DER) including air-to-water heat pumps, electric heaters and batteries. A collection of building energy models makes up a virtual district (a.k.a neighborhood or community). In each building, space cooling, space heating and domestic hot water end-use loads may be independently satisfied through air-to-water heat pumps. Alternatively, space heating and domestic hot water loads can be satisfied through electric heaters. 
 
-.. image:: ../../../assets/images/citylearn_systems.png
-   :alt: demand response
+.. image:: ../../../assets/images/environment.jpg
+   :alt: CityLearn building model including electricity sources that power controllable DERs including electric devices and ESSs, used to satisfy thermal and electrical loads as well as provide the grid with energy flexibility. A distinction is made between environment and control aspects of a building to show the transfer of actions from the control agent and reception of measurable observations by the control agent that quantifies the building's states.
    :align: center
 
 Buildings may have a combination of thermal storage tanks and batteries for active energy storage that provide continuous load shifting energy flexibility services. These storage devices may be used at peak or expensive periods to meet space cooling, space heating, domestic hot water and non-shiftable (plug) loads. The storage devices are charged by the cooling or heating devices (heat pump or electric heater) that satisfies the end-use the stored energy is for. All electric devices as well as plug loads consume electricity from the main grid. Photovoltaic (PV) arrays may be included in the buildings to offset all or part of the electricity consumption from the grid by allowing the buildings to generate their own electricity.
@@ -20,4 +20,6 @@ Since CityLearn version :code:`2.0.0`, building indoor dry-bulb temperature can
 
 Since CityLearn version :code:`2.1.0`, power outages can be simulated where buildings can only make of their available distributed energy resources including storage devices and PV system to satisfy end-use loads otherwise, risk thermal discomfort and unserved energy during the outage period. During normal operation i.e., when there is no power outage, there is unlimited supply from the grid.
 
+Since CityLearn version :code:`2.2.0`, schema datasets can be generated using the End Use Load Profiles for the U.S. Building Stock dataset. Also, electric vehicle (EV) loads and occupant thermostat overrides have been modeled and can be included in the schema and control simulation.
+
 RBC, RL or MPC agent(s) control the active storage devices by determining how much energy to store or release, and the cooling and heating device by determining their supply power at each control time step.