fix TypeError in agents and networks gis example

gis/agents_and_networks/references/GMU-Social.nlogo

@@ -54,7 +54,7 @@ vertices-own [
   entrance?  ;;if it is an entrance to a building
   test  ;;used to delete in test
 
-  ;;the follwoing variables are used and renewed in each path-selection
+  ;;the following variables are used and renewed in each path-selection
   dist  ;;distance from original point to here
   done ;;1 if has calculated the shortest path through this point, 0 otherwise
   lastnode ;;last node to this point in shortest path
@@ -108,7 +108,7 @@ to setup
 
   ;;ask patches with [ centroid? = true][sprout 1 [set size 2 set color red]] ;;use this line to verify
 
-  ;;create turtles representing the nodes. create links to conect them.
+  ;;create turtles representing the nodes. create links to connect them.
   foreach gis:feature-list-of gmu-walkway [ road-feature ->
     foreach gis:vertex-lists-of road-feature [ v ->  ; for the road feature, get the list of vertices
       let previous-node-pt nobody
@@ -813,7 +813,7 @@ PLOT
 456
 739
 672
-Firends at Home
+Friends at Home
 No. of friends at home
 Count of people
 0.0
@@ -852,15 +852,15 @@ You may want to turn off some layers for a clear display.
 
 ## THINGS TO TRY
 
-Change the switches for different dispalys. Try different number of coimmuters. Try the verification.
+Change the switches for different displays. Try different number of coimmuters. Try the verification.
 
 ## EXTENDING THE MODEL
 
 What if the commuters move with a speed (some distance per tick) instead of one node per tick?
 
 ## NETLOGO FEATURES
 
-For faster compuation, this model simplifies the original data by reducing the number of nodes. To do that, the walkway data is loaded to the 20 x 20 grid in Netlogo, which is small, and therefore, many nodes fall on the same patch. In each patch, we only want to keep one node, and duplicate nodes are removed, while their neighbors are connected to the one node left.
+For faster computation, this model simplifies the original data by reducing the number of nodes. To do that, the walkway data is loaded to the 20 x 20 grid in Netlogo, which is small, and therefore, many nodes fall on the same patch. In each patch, we only want to keep one node, and duplicate nodes are removed, while their neighbors are connected to the one node left.
 
 Also, links are created in this model to represent raods. This is so far the best way I can find to deal with road related problems in Netlogo. However, because the way I create links is to link nodes one by one (see code for more details), so some roads are likely to be left behind. But again there is no better way I can find. Therefore, I also used a loop in setup to delete nodes that are not connected to the whole network.
 

gis/agents_and_networks/src/space/utils.py

@@ -78,7 +78,9 @@ class UnitTransformer:
     _degree2meter: pyproj.Transformer
     _meter2degree: pyproj.Transformer
 
-    def __init__(self, degree_crs: pyproj.CRS | None, meter_crs: pyproj.CRS | None):
+    def __init__(
+        self, degree_crs: pyproj.CRS | None = None, meter_crs: pyproj.CRS | None = None
+    ):
         if degree_crs is None:
             degree_crs = pyproj.CRS("EPSG:4326")
 

rl/README.md

@@ -50,7 +50,7 @@ To test the code, simply execute `example.py`:
 python example.py
 ```
 
-*Note: Pre-trained models might not work in some cases because of differnce in versions of libraries used to train and test.*
+*Note: Pre-trained models might not work in some cases because of difference in versions of libraries used to train and test.*
 
 To learn about individual implementations, please refer to the README files of specific environments.
 

rl/boltzmann_money/server.py

@@ -9,8 +9,8 @@
 
 # Modify the MoneyModel class to take actions from the RL model
 class MoneyModelRL(BoltzmannWealthModelRL):
-    def __init__(self, N, width, height):
-        super().__init__(N, width, height)
+    def __init__(self, n, width, height):
+        super().__init__(n, width, height)
         model_path = os.path.join(
             os.path.dirname(__file__), "..", "model", "boltzmann_money.zip"
         )

rl/epstein_civil_violence/README.md

@@ -8,7 +8,7 @@ This project demonstrates the use of the RLlib library to implement Multi-Agent
 - **Library Utilized**: The project leverages the RLlib library to concurrently train two independent PPO (Proximal Policy Optimization) agents.
 - **Agents**:
   - **Police**: Aims to control violence (Reduce active agent)
-  - **Citizen**: Aims to show resistence (be active) without getting arrested
+  - **Citizen**: Aims to show resistance (be active) without getting arrested
 
 **Input and Observation Space**:
 - **Observation Grid**: Each agent's policy receives a 4 radius grid centered on itself as input.

rl/epstein_civil_violence/agent.py

@@ -1,5 +1,6 @@
 from mesa.examples.advanced.epstein_civil_violence.agents import Citizen, Cop
-from utility import move
+
+from .utility import move
 
 
 class CitizenRL(Citizen):
@@ -11,7 +12,7 @@ def step(self):
             self.jail_sentence -= 1
         else:
             # RL Logic
-            # Update condition and postion based on action
+            # Update condition and position based on action
             self.condition = "Active" if action_tuple[0] == 1 else "Quiescent"
             # Update neighbors for updated empty neighbors
             self.update_neighbors()

rl/epstein_civil_violence/model.py

@@ -4,7 +4,8 @@
 from agent import CitizenRL, CopRL
 from mesa.examples.advanced.epstein_civil_violence.model import EpsteinCivilViolence
 from ray.rllib.env import MultiAgentEnv
-from utility import create_intial_agents, grid_to_observation
+
+from .utility import create_initial_agents, grid_to_observation
 
 
 class EpsteinCivilViolenceRL(EpsteinCivilViolence, MultiAgentEnv):
@@ -143,8 +144,8 @@ def reset(self, *, seed=None, options=None):
         """
         super().reset()
         self.grid = mesa.space.SingleGrid(self.width, self.height, torus=True)
-        create_intial_agents(self, CitizenRL, CopRL)
-        grid_to_observation(self, CitizenRL)
+        create_initial_agents(self)
+        grid_to_observation(self)
         # Initialize action dictionary with no action
         self.action_dict = {a.unique_id: (0, 0) for a in self.agents}
         # Update neighbors for observation space

rl/epstein_civil_violence/utility.py

@@ -1,11 +1,14 @@
-def create_intial_agents(self, CitizenRL, CopRL):
+from .agent import CitizenRL, CopRL
+
+
+def create_initial_agents(self):
     # Create agents
     unique_id = 0
     if self.cop_density + self.citizen_density > 1:
         raise ValueError("CopRL density + citizen density must be less than 1")
     cops = []
     citizens = []
-    for contents, (x, y) in self.grid.coord_iter():
+    for _, (x, y) in self.grid.coord_iter():
         if self.random.random() < self.cop_density:
             unique_id_str = f"cop_{unique_id}"
             cop = CopRL(unique_id_str, self, (x, y), vision=self.cop_vision)
@@ -35,7 +38,7 @@ def create_intial_agents(self, CitizenRL, CopRL):
         self.add(citizen)
 
 
-def grid_to_observation(self, CitizenRL):
+def grid_to_observation(self):
     # Convert neighborhood to observation grid
     self.obs_grid = []
     for i in self.grid._grid:

rl/train.py

@@ -36,7 +36,7 @@ def train_model(
     algo_config = get_config(config)
     algo = algo_config.build()
 
-    for i in range(num_iterations):
+    for _ in range(num_iterations):
         result = algo.train()
         print(pretty_print(result))
 

rl/wolf_sheep/README.md

@@ -7,7 +7,7 @@ This project demonstrates the use of the RLlib library to implement Multi-Agent
 **RLlib and Multi-Agent Learning**:
 - **Library Utilized**: The project leverages the RLlib library to concurrently train two independent PPO (Proximal Policy Optimization) agents.
 - **Agents**:
-  - **Wolf**: Predatory agent survives by eating sheeps
+  - **Wolf**: Predatory agent survives by eating sheep
   - **Sheep**: Prey agent survives by eating grass
   - **Grass**: Grass is eaten by sheep and regrows with time
 

rl/wolf_sheep/agents.py

@@ -1,5 +1,6 @@
 from mesa.examples.advanced.wolf_sheep.agents import GrassPatch, Sheep, Wolf
-from utility import move
+
+from .utility import move
 
 
 class SheepRL(Sheep):

rl/wolf_sheep/model.py

@@ -6,7 +6,8 @@
 from mesa.examples.advanced.wolf_sheep.model import WolfSheep
 from mesa.experimental.devs import ABMSimulator
 from ray.rllib.env import MultiAgentEnv
-from utility import create_intial_agents, grid_to_observation
+
+from .utility import create_initial_agents, grid_to_observation
 
 # Don't create the ABMSimulator as argument default: https://docs.astral.sh/ruff/rules/function-call-in-default-argument/
 ABM_SIMULATOR = ABMSimulator()
@@ -154,8 +155,8 @@ def reset(self, *, seed=None, options=None):
         super().reset()
         self.grid = mesa.space.MultiGrid(self.width, self.height, torus=True)
         self.current_id = 0
-        create_intial_agents(self, SheepRL, WolfRL, GrassPatch)
-        grid_to_observation(self, SheepRL, WolfRL, GrassPatch)
+        create_initial_agents(self)
+        grid_to_observation(self)
         obs = {}
         for agent in self.agents:
             if isinstance(agent, (SheepRL, WolfRL)):

rl/wolf_sheep/utility.py

@@ -1,6 +1,9 @@
-def create_intial_agents(self, SheepRL, WolfRL, GrassPatch):
+from .agents import GrassPatch, SheepRL, WolfRL
+
+
+def create_initial_agents(self):
     # Create sheep:
-    for i in range(self.initial_sheep):
+    for _ in range(self.initial_sheep):
         x = self.random.randrange(self.width)
         y = self.random.randrange(self.height)
         energy = self.random.randrange(2 * self.sheep_gain_from_food)
@@ -10,7 +13,7 @@ def create_intial_agents(self, SheepRL, WolfRL, GrassPatch):
         self.add(sheep)
 
     # Create wolves
-    for i in range(self.initial_wolves):
+    for _ in range(self.initial_wolves):
         x = self.random.randrange(self.width)
         y = self.random.randrange(self.height)
         energy = self.random.randrange(2 * self.wolf_gain_from_food)
@@ -21,7 +24,7 @@ def create_intial_agents(self, SheepRL, WolfRL, GrassPatch):
 
     # Create grass patches
     if self.grass:
-        for agent, (x, y) in self.grid.coord_iter():
+        for _, (x, y) in self.grid.coord_iter():
             fully_grown = self.random.choice([True, False])
 
             if fully_grown:
@@ -62,7 +65,7 @@ def move(self, action):
         self.model.grid.move_agent(self, new_position)
 
 
-def grid_to_observation(self, SheepRL, WolfRL, GrassPatch):
+def grid_to_observation(self):
     # Convert grid to matrix for better representation
     self.obs_grid = []
     for i in self.grid._grid: