Experiments (#20)

* Added more experiments

* Extra df

* added more analyse

* Added memory delay to experiments

* Dataframe Explored analyse

* Added Graphing function

* Added more analyse

* added Leaftraking policy to the experiments

* Added more text to the notebook for the demo.

* Added helpers functions to analyse data.

* Changed folder name.

* Deleted extra notebook

Co-authored-by: MariaDukmak <mariadukmak@student.hu.nl>
Co-authored-by: Jellestiesri <jellestiesri@gmail.com>
Co-authored-by: RichardDev01 <60653249+RichardDev01@users.noreply.github.com>

README.md

@@ -11,7 +11,7 @@ This git repository houses the challenges for the course *Autonomy By Design*.
 The simulation is made out of two parts, the `Environment` and the `Policy`.
 The image below illustrates the simulation loop:
 
-![](https://github.com/MariaDukmak/MazeRunner/blob/Readme-uitwerken/images/schets_simulatie.png)
+![](https://github.com/MariaDukmak/MazeRunner/blob/Readme-uitwerken/readme_assets/schets_simulatie.png)
 
 
 ### Installation

analysis/helpers/__init__.py

@@ -0,0 +1 @@
+from analysis.helpers.helper import plot_batch, read_data

analysis/helpers/helper.py

@@ -0,0 +1,32 @@
+"""Helpers function to analyse experiment data."""
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+
+
+def read_data(experiment_name: str) -> pd.DataFrame:
+    """Read the batch data from a pickle."""
+    return pd.read_pickle(f'experiments/{experiment_name}/batch_data.p')
+
+
+def count_explored_tiles_agents(env_df: pd.DataFrame) -> dict:
+    """Count how many tiles a agent has explored per tik."""
+    dict_agent_info = dict()
+    for agent in range(env_df['agents_n'].max()):
+        list_agent_info = []
+        for i in range(len(env_df)):
+            list_agent_info.append(env_df.iloc[i]['explored'][agent].sum())
+        dict_agent_info[agent] = list_agent_info
+    return dict_agent_info
+
+
+def plot_batch(env_df: pd.DataFrame) -> None:
+    """Plot result per batch."""
+    for batch in range(len(env_df)):
+        sub_df = pd.DataFrame.from_dict(env_df[batch]).set_index('time')
+        fig, axes = plt.subplots(1, 1, figsize=(5, 5))
+        fig.suptitle('Explored maze by agents')
+        plt.xlabel("time step")
+        plt.ylabel("explored tiles")
+        sns.lineplot(axes=axes, data=count_explored_tiles_agents(sub_df))
+        plt.show()