it is working

ai_economist/foundation/entities/resources.py

@@ -65,3 +65,20 @@ class Coin(Resource):
     color = np.array([229, 211, 82]) / 255.0
     collectible = False
 
+@resource_registry.add
+class GemRaw(Resource):
+    """Raw Gem that can be processed further"""
+
+    name = "Gem_Raw"
+    color = np.array([241, 233, 219]) / 255.0
+    collectible = True
+
+@resource_registry.add
+class Gem(Resource):
+    """Proccesed Gem. Craftable."""
+
+    name = "Gem"
+    color = np.array([241, 233, 219]) / 255.0
+    collectible = False
+    craft_recp= {"Gem_Raw": 1}
+    craft_labour_base= 1

components/crafting.py

@@ -238,7 +238,7 @@ class Craft(BaseComponent):
        
             
         for agent in world.agents:
-                if agent.name not in self.agent_subclasses | agent.is_setup():
+                if agent.name not in self.agent_subclasses | agent.is_setup:
                     continue
                 agent.state["craft_skill"]={}
                 agent.state["craft_labour"]={}

envs/__init__.py

@@ -1,5 +1,6 @@
 
 from . import (
     simple_market,
-    econ_wrapper
+    econ_wrapper,
+    econ
 )

envs/econ.py

@@ -266,7 +266,7 @@ class Econ(BaseEnvironment):
         for agent in self.world.agents:
             if not self._persist_between_resets:
                 agent.set_setup(False) # resets agent states
-            if not agent.is_setup(): # agent has not been setup for scenario
+            if not agent.is_setup: # agent has not been setup for scenario
                 agent.state["inventory"] = {k: 0 for k in agent.inventory.keys()}
                 agent.state["escrow"] = {k: 0 for k in agent.inventory.keys()}
                 agent.state["endogenous"] = {k: 0 for k in agent.endogenous.keys()}

main.py

@@ -7,6 +7,7 @@ from stable_baselines3.common.evaluation import evaluate_policy
 from sb3_contrib.ppo_mask import MaskablePPO
 import envs
 import wrapper
+import resources
 from wrapper.base_econ_wrapper import BaseEconWrapper
 from wrapper.reciever_econ_wrapper import RecieverEconWrapper
 from wrapper.sb3_econ_converter import SB3EconConverter
@@ -26,7 +27,7 @@ env_config = {
     # ===== SCENARIO CLASS =====
     # Which Scenario class to use: the class's name in the Scenario Registry (foundation.scenarios).
     # The environment object will be an instance of the Scenario class.
-    'scenario_name': 'simple_market',
+    'scenario_name': 'econ',
     
     # ===== COMPONENTS =====
     # Which components to use (specified as list of ("component_name", {component_kwargs}) tuples).
@@ -78,7 +79,7 @@ eval_env_config = {
     # ===== SCENARIO CLASS =====
     # Which Scenario class to use: the class's name in the Scenario Registry (foundation.scenarios).
     # The environment object will be an instance of the Scenario class.
-    'scenario_name': 'simple_market',
+    'scenario_name': 'econ',
     
     # ===== COMPONENTS =====
     # Which components to use (specified as list of ("component_name", {component_kwargs}) tuples).
@@ -231,23 +232,24 @@ model = MaskablePPO("MlpPolicy",n_steps=int(env_config['episode_length']*2),ent_
 n_agents=econ.n_agents
 total_required_for_episode=n_agents*env_config['episode_length']
 print("this is run {}".format(runname))
+eval_econ=foundation.make_env_instance(**eval_env_config)
+eval_base_econ=BaseEconWrapper(eval_econ)
+eval_base_econ.run()
+eval_mobileRecieverEconWrapper=RecieverEconWrapper(eval_base_econ,"BasicMobileAgent")
+time.sleep(0.5)
+eval_sb3_converter=SB3EconConverter(eval_mobileRecieverEconWrapper,eval_econ,"BasicMobileAgent")
 while True:
     # Create Eval ENV
-  
-    vec_env_eval=EconVecEnv(env_config=eval_env_config)
-    vec_mon_eval=VecMonitor(venv=vec_env_eval)
-    norm_env_eval=VecNormalize(vec_mon_eval,norm_reward=False,training=False)
-    eval_econ = vec_env_eval.env
-  
+    vec_mon_eval=VecMonitor(venv=eval_sb3_converter)
     #Train
-    model=model.learn(total_timesteps=total_required_for_episode*50,progress_bar=True,reset_num_timesteps=False,tb_log_name=runname,callback=TensorboardCallback(econ=econ))
+    model=model.learn(total_timesteps=total_required_for_episode*20,progress_bar=True,reset_num_timesteps=False,tb_log_name=runname,callback=TensorboardCallback(econ=econ))
     #normenv.save("temp-normalizer.ai")
 
     
    
     ## Run Eval
     print("### EVAL ###")
-    norm_env_eval.load("temp-normalizer.ai",vec_mon_eval)
+
     obs=vec_mon_eval.reset()
     done=False
     for i in tqdm(range(eval_env_config['episode_length'])):

resources/resources.py

@@ -1,23 +1,4 @@
 
 import numpy as np
-
-
 from ai_economist.foundation.entities.resources import Resource, resource_registry
 
-@resource_registry.add
-class RawGem(Resource):
-    """Raw Gem that can be processed further"""
-
-    name = "Raw_Gem"
-    color = np.array([241, 233, 219]) / 255.0
-    collectible = True
-
-@resource_registry.add
-class Gem(Resource):
-    """Proccesed Gem. Craftable."""
-
-    name = "Gem"
-    color = np.array([241, 233, 219]) / 255.0
-    collectible = False
-    craft_recp= {"Raw_Gem": 1}
-    craft_labour_base= 1