minor fixes in learning documentation and inclusion of more detailed …

…example description table + deleted unnecessary learning example

README.md

@@ -17,7 +17,7 @@ SPDX-License-Identifier: AGPL-3.0-or-later
 [![](https://img.shields.io/pypi/status/assume-framework.svg)](https://pypi.org/pypi/assume-framework/)
 [![](https://img.shields.io/readthedocs/assume)](https://assume.readthedocs.io/)
 
-[![Open Tutorials In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1LISiM1QvDIMXU68pJH-NqrMw5w7Awb24?usp=sharing)
+[![Open Learning Tutorial](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1LISiM1QvDIMXU68pJH-NqrMw5w7Awb24?usp=sharing)
 
 **ASSUME** is an open-source toolbox for agent-based simulations of European electricity markets, with a primary focus on the German market setup. Developed as an open-source model, its primary objectives are to ensure usability and customizability for a wide range of users and use cases in the energy system modeling community.
 
@@ -89,7 +89,7 @@ How to configure a new unit in ASSUME?
 
 How to intorduce reinforcement learning to ASSUME?
 
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1LISiM1QvDIMXU68pJH-NqrMw5w7Awb24?usp=sharing)
+[![Open Learning Tutorial](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1LISiM1QvDIMXU68pJH-NqrMw5w7Awb24?usp=sharing)
 
 
 

docs/source/example_simulations.rst

@@ -6,7 +6,7 @@ Example Simulations
 =====================
 
 While the modeller can define her own input data for the simulation, we provide some example simulations to get started.
-Here you can find an overview of the different exampels provided.
+Here you can find an overview of the different exampels provided. Below you find an exhaustive table explaining the different examples.
 
 
  ============================= ============================= =====================================================
@@ -17,7 +17,21 @@ Here you can find an overview of the different exampels provided.
   small_with_opt_clearing       example_01a                     Small simulation with optimization clearing instead of pay_as_clear.
   small_with_BB                 example_01e                     Small Simulation with Block Bids and complex clearing.
   small_with_vre                example_01b                     Small simulation with variable renewable energy.
-                                example_01c                     A small study with CRM markets
-  learning_small                example_02a                     A small study with roughly 10 powerplants, where one powerplant is equiped with a learning bidding strategy and can learn to exert market power.
-  learning_medium               example_02b                     A small study with roughly 10 powerplants, where multiple powerplants are equiped with a learning bidding strategy and learn that they do not have market power anymore.
+  small_learning_1              example_02a                     A small study with roughly 10 powerplants, where one powerplant is equiped with a learning bidding strategy and can learn to exert market power.
+  small_learning_2              example_02b                     A small study with roughly 10 powerplants, where multiple powerplants are equiped with a learning bidding strategy and learn that they do not have market power anymore.
  ============================= ============================= =====================================================
+
+The following table categorizes the different provided examples in a more detailed manner. We included the main features of ASSUME in the table.
+
+
+============================== =============== =============== =================== ====================== ============= ============= ================= ============== =============
+example name                   Country         Generation Tech Generation Volume   Demand Tech            Demand Volume Markets       Bidding Strategy  Grid           Further Infos
+============================== =============== =============== =================== ====================== ============= ============= ================= ============== =============
+small_learning_1               Germany         conventional    12,500 MW           fixed inflexible       1,000,000 MW  EoM           Learning, Naive   No             Resembles Case 1 from Harder et.al. 2023
+small_learning_2               Germany         conventional    12,500 MW           fixed inflexible       1,000,000 MW  EoM           Learning, Naive   No             Resembles Case 2 from Harder et.al. 2023
+============================== =============== =============== =================== ====================== ============= ============= ================= ============== =============
+
+
+References
+-----------
+Harder, Nick & Qussous, Ramiz & Weidlich, Anke. (2023). Fit for purpose: Modeling wholesale electricity markets realistically with multi-agent deep reinforcement learning. Energy and AI. 14. 100295. 10.1016/j.egyai.2023.100295.

docs/source/learning.rst

@@ -9,7 +9,10 @@ Reinforcement Learning
 One unique characteristic of ASSUME is the usage of Reinforcement Learning (RL) for the bidding of the agents.
 To enable this the architecture of the simulation is designed in a way to accommodate the learning process. In this part of
 the documentation, we give a short introduction to reinforcement learning in general and then pinpoint you to the
-relevant parts of the code. If you want a hands-on introduction check out the prepared tutorial in Colab: https://colab.research.google.com/drive/1LISiM1QvDIMXU68pJH-NqrMw5w7Awb24?usp=sharing
+relevant parts of the code. the descriptions are mostly based on the following paper
+Harder, Nick & Qussous, Ramiz & Weidlich, Anke. (2023). Fit for purpose: Modeling wholesale electricity markets realistically with multi-agent deep reinforcement learning. Energy and AI. 14. 100295. 10.1016/j.egyai.2023.100295.
+
+If you want a hands-on introduction check out the prepared tutorial in Colab: https://colab.research.google.com/drive/1LISiM1QvDIMXU68pJH-NqrMw5w7Awb24?usp=sharing
 
 
 The Basics of Reinforcement Learning
@@ -81,10 +84,10 @@ where L is the loss function.
 
 The actor and critic networks are trained simultaneously using the actor-critic algorithm, which updates the weights of
 both networks at each time step. The actor-critic algorithm is a form of policy iteration, where the policy is updated based on the
-estimated value function, and the value function is updated based on the.
+estimated value function, and the value function is updated based on the critic.
 
 
-1.2 Multi-Agent Learning
+Multi-Agent Learning
 ------------------------
 
 In a single-agent setup, the state transition and respective reward depend only on the actions of a single agent. However, in a

examples/examples.py

@@ -47,8 +47,8 @@
         "scenario": "example_02",
         "study_case": "dam_case_2019",
     },
-    "learning_small": {"scenario": "example_02a", "study_case": "base"},
-    "learning_medium": {"scenario": "example_02b", "study_case": "base"},
+    "small_learning_1": {"scenario": "example_02a", "study_case": "base"},
+    "small_learning_2": {"scenario": "example_02b", "study_case": "base"},
 }
 
 # %%