cleanup

Author: doesburg11

.pre-commit-config.yaml

@@ -0,0 +1,11 @@
+repos:
+- repo: https://github.com/pre-commit/pre-commit-hooks
+  rev: v4.0.1
+  hooks:
+  - id: trailing-whitespace
+  - id: end-of-file-fixer
+  - id: check-yaml
+- repo: https://github.com/psf/black
+  rev: 23.7.0
+  hooks:
+  - id: black

pettingzoo/predpreygrass/api_test.py

@@ -1,6 +1,6 @@
 # passed API test for predpreygrass
 from pettingzoo.test import api_test
-import environments.predpreygrass_available_energy_transfer as predpreygrass
+import environments.predpreygrass_default as predpreygrass
 
 env = predpreygrass.raw_env()
 api_test(env, num_cycles=1000, verbose_progress=False)

pettingzoo/predpreygrass/archive/optuna_test_20240531.py

@@ -1,6 +1,6 @@
 import environments.predpreygrass_variable_energy_transfer as predpreygrass
 
-from config.config_pettingzoo import (
+from pettingzoo.predpreygrass.config.config_predpreygrass_default import (
     env_kwargs,
     training_steps_string,
     local_output_directory,

pettingzoo/predpreygrass/config/README.md

@@ -1,3 +1,3 @@
 ### Configurations
 
-Configurations and their consequences emergent behaviors are [discussed on our website](https://www.behaviorpatterns.info/predator-prey-grass-project/).
+Configurations and their consequentialy emergent behaviors are [discussed on our website](https://www.behaviorpatterns.info/predator-prey-grass-project/).

pettingzoo/predpreygrass/config/config_pettingzoo_walls.py

@@ -1,13 +1,16 @@
 # put in here your own directory to the output folder
 local_output_directory = "/home/doesburg/Dropbox/02_marl_results/predpreygras_results/"
 
-training_steps_string = "30_000_000"
+training_steps_string = "10_000_000"
+
+x_grid_size = 25
+y_grid_size = 25
 
 env_kwargs = dict(
     # environment parameters
     max_cycles=5000, 
-    x_grid_size=25,
-    y_grid_size=25, 
+    x_grid_size=x_grid_size,
+    y_grid_size=y_grid_size, 
     # agent parameters
     n_possible_predator=18,  # maximum number of predators during runtime
     n_possible_prey=24,
@@ -19,7 +22,7 @@
     obs_range_predator=7,  
     obs_range_prey=9, 
     # energy parameters
-    energy_gain_per_step_predator = -0.15, # optimized by parameter variation [-0.3,-0.25,-0.20,-0.15,-0.10]
+    energy_gain_per_step_predator = -0.15,  # optimized by parameter variation [-0.3,-0.25,-0.20,-0.15,-0.10]
     energy_gain_per_step_prey = -0.05,  
     energy_gain_per_step_grass = 0.2,  
     catch_prey_energy = 0.0,
@@ -52,22 +55,21 @@
     spawning_area_predator = dict({
             "x_begin": 0,
             "y_begin": 0,
-            "x_end": 24,
-            "y_end": 24,
+            "x_end": x_grid_size-1,
+            "y_end": y_grid_size-1,
         }),
     spawning_area_prey = dict({
             "x_begin": 0,
             "y_begin": 0,
-            "x_end": 24,
-            "y_end": 24,
+            "x_end": x_grid_size-1,
+            "y_end": y_grid_size-1,
         }), 
     spawning_area_grass = dict({
             "x_begin": 12,
             "y_begin": 0,
             "x_end": 13,
-            "y_end": 24,
+            "y_end": y_grid_size-1,
         }),
 
 
-
 )

pettingzoo/predpreygrass/config/config_pettingzoo.py pettingzoo/predpreygrass/config/config_predpreygrass_default.py

@@ -12,8 +12,8 @@
 
 """
 # Continue training for X steps and log the results to TensorBoard
-import environments.predpreygrass_available_energy_transfer as predpreygrass
-from config.config_pettingzoo import env_kwargs, training_steps_string
+import environments.predpreygrass_default as predpreygrass
+from config.config_predpreygrass_default import env_kwargs, training_steps_string
 
 import os
 

pettingzoo/predpreygrass/continue_training_saved_model.py

@@ -11,7 +11,5 @@ Learning agents (Predators and Prey) receive fixed pre determined energy and rew
 The removal or creation of Predators or Prey is handeled by the `is_active` boolean of the agents.
 At `reset`,`n_possible_predator` and `n_possible_prey` are initialized. However, a portion of agents is intialized at `is_active` = `False`, this will give room for creation of agents during runtime. Conversely, removal of agents during runtime is handled by setting the attribute `is_active` from `True` to `False`. The rewards and observations of non active agents are zeros, comparable with SuperSuit's Black Death wrapper.
 
-`predatorpreygrass_available_energy_transfer.py`: A generalization of 'predatorpreygrass_fixed_energy_transfer.py'. Rather than receiving fixed predeterimined energy quantities, agents exchange energy depending on the energy available from the eaten agents. The observation channels consists of the energy levels of agents, rather than just one's or zero's in 'predatorpreygrass_fixed_energy_transfer.py'. The purpose of this generalization is to gauge wether agents maken energy-efficiency choices. So, if they go after a resource which has higher energetic value than antother within their observation range.
+`predatorpreygrass.py`: The default environment. A generalization of 'predatorpreygrass_fixed_energy_transfer.py'. Rather than receiving fixed predeterimined energy quantities, agents exchange energy depending on the energy available from the eaten agents. The observation channels consists of the energy levels of agents, rather than just one's or zero's in 'predatorpreygrass_fixed_energy_transfer.py'. The purpose of this generalization is to gauge wether agents maken energy-efficiency choices. So, if they go after a resource which has higher energetic value than antother within their observation range.
 
-
-`predatorpreygrass_available_energy_transfer_walls.py`: The action space is extended where Predators as well as Prey can build obstacles or walls in the grid world environment at the expense of a certain amount of energy for the agent. So the action space for learning agents is ['MOVE UP', 'MOVE DOWN', 'MOVE LEFT', 'MOVE RIGHT', 'STAY', 'BUILD WALL']