i think i hav repaired for current stuff

main.py

@@ -209,13 +209,14 @@ baseEconWrapper=BaseEconWrapper(econ)
 baseEconWrapper.run()
 mobileRecieverEconWrapper=RecieverEconWrapper(base_econ=baseEconWrapper,agent_classname="BasicMobileAgent")
 sb3Converter=SB3EconConverter(mobileRecieverEconWrapper,econ,"BasicMobileAgent")
-obs=sb3Converter.reset()
-vecenv=EconVecEnv(env_config=env_config)
+#obs=sb3Converter.reset()
+#vecenv=EconVecEnv(env_config=env_config)
 
-monenv=VecMonitor(venv=vecenv,info_keywords=["social/productivity","trend/productivity"])
-normenv=VecNormalize(monenv,norm_reward=False,clip_obs=1)
-stackenv=vec_frame_stack.VecFrameStack(venv=monenv,n_stack=10)
-obs=stackenv.reset()
+monenv=VecMonitor(venv=sb3Converter,info_keywords=["social/productivity","trend/productivity"])
+
+#normenv=VecNormalize(sb3Converter,norm_reward=False,clip_obs=1)
+#stackenv=vec_frame_stack.VecFrameStack(venv=monenv,n_stack=10)
+obs=monenv.reset()
 
 
 
@@ -224,8 +225,8 @@ obs=stackenv.reset()
 runname="run_{}".format(int(np.random.rand()*100))
 
 model = PPO("MlpPolicy",n_steps=int(env_config['episode_length']*2),ent_coef=0.1, vf_coef=0.8 ,gamma=0.95, learning_rate=5e-3,env=monenv, verbose=1,device="cuda",tensorboard_log="./log")
-
-total_required_for_episode=env_config['n_agents']*env_config['episode_length']
+n_agents=econ.n_agents
+total_required_for_episode=n_agents*env_config['episode_length']
 print("this is run {}".format(runname))
 while True:
     # Create Eval ENV
@@ -237,7 +238,7 @@ while True:
   
     #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))
-    normenv.save("temp-normalizer.ai")
+    #normenv.save("temp-normalizer.ai")
 
     
    

wrapper/base_econ_wrapper.py

@@ -88,10 +88,13 @@ class BaseEconWrapper():
 
             #check for actions
             self.action_edit_lock.acquire()
-            if self.env.n_agents==len(self.actor_actions.keys) & self.step_notification.is_set()==False:
+            enough_votes_for_step=self.env.n_agents==len(self.actor_actions.keys())
+            currently_not_stepping=self.step_notification.is_set()==False
+            self.action_edit_lock.release() 
+            if  enough_votes_for_step & currently_not_stepping:
                 # we have all the actions -> STEP
                self._step()
-            self.action_edit_lock.release() # release actions
+           # release actions
             # we are done
 
     def stop_env(self):
@@ -137,8 +140,8 @@ class BaseEconWrapper():
         """Submits actions to base processing queue. Actions as dict pairing of idx and action id"""
         self._prepare_step() # New actions are bening submitted. Prepare base for new step
         self.action_edit_lock.acquire() # Start to submit action dict       
-        for k,v in actions:
-            if self.actor_actions[k]!=None:
+        for k,v in actions.items():
+            if k in self.actor_actions.keys():
                raise Exception("Actor action has already been submitted. {}".format(k))
             self.actor_actions[k]=v
         self.base_notification.set() #Alert base for action changes

wrapper/reciever_econ_wrapper.py

@@ -52,7 +52,7 @@ class RecieverEconWrapper(gym.Env):
         obs,rew,done,info=self.base_econ.reciever_block_step()
         c_obs=self._dict_idx_to_index(obs)
         c_rew=self._dict_idx_to_index(rew)
-        c_done=self._dict_idx_to_index(done)
+        c_done=done
         c_info=self._dict_idx_to_index(info)
         return c_obs,c_rew,c_done,c_info
 

wrapper/sb3_econ_converter.py

@@ -19,7 +19,8 @@ class SB3EconConverter(VecEnv, gym.Env):
         self.packager=utils.build_packager(obs[0])
         #flatten obervation of first agent 
         obs0=utils.package(obs[0],*self.packager)
-        self.observation_space=gym.spaces.Box(low=-np.inf,high=np.inf,shape=(len(obs0),1),dtype=np.float32)
+        obs0["flat"]
+        self.observation_space=gym.spaces.Box(low=-np.inf,high=np.inf,shape=(len(obs0["flat"]),),dtype=np.float32)
         super().__init__(self.num_envs,  self.observation_space, self.action_space)
        
     
@@ -43,16 +44,17 @@ class SB3EconConverter(VecEnv, gym.Env):
         
         c_obs=utils.convert_econ_to_gym(f_obs)
         c_rew=utils.convert_econ_to_gym(rew)
-        c_done=utils.convert_econ_to_gym(done)
+        c_done={}
         c_info=utils.convert_econ_to_gym(info)
         done_g=[False]*self.num_envs
         done=(done["__all__"])
         if done:
             for i in range(self.num_envs):
-                c_done[i]=done
+                done_g[i]=done
                 c_info[i]["terminal_observation"]=c_obs[i]
             c_obs=self.reset()
-        return c_obs,c_rew,c_done,c_info
+        return np.copy(c_obs),np.copy(c_rew),np.copy(done_g),np.copy(c_info)
+
     def reset(self) -> VecEnvObs:
         obs=self.env.reset()
         f_obs={}
@@ -62,7 +64,7 @@ class SB3EconConverter(VecEnv, gym.Env):
         for k,v in f_obs.items():
             g_obs[k]=v["flat"]
         c_obs=utils.convert_econ_to_gym(g_obs)
-        return c_obs
+        return np.copy(c_obs)
     
     def seed(self, seed: Optional[int] = None) -> List[Union[None, int]]:
         if seed is None:

wrapper/utils.py

@@ -7,8 +7,8 @@ def convert_econ_to_gym(econ):
 
 def convert_gym_to_econ(gy):
         econ={}
-        for k,v in gy:
-            econ[k]=v
+        for k in range(len(gy)):
+            econ[k]=gy[k]
         return econ