entropy you silly nugget

test.py

@@ -39,7 +39,7 @@ env_config = {
     # The order in which components reset, step, and generate obs follows their listed order below.
     'components': [
         # (1) Building houses
-        ('Craft', {'skill_dist': "pareto", 'commodities': ["Gem"],'max_skill_amount_benefit':1.5}),
+        ('Craft', {'skill_dist': "pareto", 'commodities': ["Gem"],'max_skill_amount_benefit':2}),
         # (2) Trading collectible resources
         ('ContinuousDoubleAuction', {'max_num_orders': 10}),
         # (3) Movement and resource collection
@@ -52,7 +52,7 @@ env_config = {
     # ===== SCENARIO CLASS ARGUMENTS =====
     # (optional) kwargs that are added by the Scenario class (i.e. not defined in BaseEnvironment)
     
-    'starting_agent_coin': 10,
+    'starting_agent_coin': 20,
     'fixed_four_skill_and_loc': True,
     
     # ===== STANDARD ARGUMENTS ======
@@ -60,6 +60,7 @@ env_config = {
     'agent_composition': {"BasicMobileAgent": 20,"TradingAgent":5},        # Number of non-planner agents (must be > 1)
     'world_size': [5, 5], # [Height, Width] of the env world
     'episode_length': 256, # Number of timesteps per episode
+    'isoelastic_eta':0.001,
     'allow_observation_scaling': True,
     'dense_log_frequency': 100, 
     'world_dense_log_frequency':1,
@@ -94,7 +95,7 @@ eval_env_config = {
     # The order in which components reset, step, and generate obs follows their listed order below.
     'components': [
         # (1) Building houses
-        ('Craft', {'skill_dist': "pareto", 'commodities': ["Gem"],'max_skill_amount_benefit':1.5}),
+        ('Craft', {'skill_dist': "pareto", 'commodities': ["Gem"],'max_skill_amount_benefit':2}),
         # (2) Trading collectible resources
         ('ContinuousDoubleAuction', {'max_num_orders': 10}),
         # (3) Movement and resource collection
@@ -107,7 +108,7 @@ eval_env_config = {
     # ===== SCENARIO CLASS ARGUMENTS =====
     # (optional) kwargs that are added by the Scenario class (i.e. not defined in BaseEnvironment)
     
-    'starting_agent_coin': 10,
+    'starting_agent_coin': 20,
     'fixed_four_skill_and_loc': True,
     
     # ===== STANDARD ARGUMENTS ======
@@ -116,6 +117,7 @@ eval_env_config = {
     'world_size': [1, 1], # [Height, Width] of the env world
     'episode_length': 256, # Number of timesteps per episode
     'allow_observation_scaling': True,
+    'isoelastic_eta':0.001,
     'dense_log_frequency': 1, 
     'world_dense_log_frequency':1, 
     'energy_cost':0,
@@ -135,7 +137,7 @@ eval_env_config = {
     'flatten_masks': True,
 }
 
-num_frames=5
+num_frames=1
 
 class TensorboardCallback(BaseCallback):
     """
@@ -161,6 +163,23 @@ class TensorboardCallback(BaseCallback):
         
         return True
 
+min_at_target_basic=0.5
+min_lr_basic=5e-6
+start_lr_basic=9e-4
+
+min_at_target_trade=0.5
+min_lr_trade=5e-6
+start_lr_trade=9e-4
+
+def learning_rate_adj_basic(x) -> float:
+    diff=start_lr_basic-min_lr_basic
+    lr=min_lr_basic+x*diff
+    return lr
+
+def learning_rate_adj_trade(x) -> float:
+    diff=start_lr_trade-min_lr_trade
+    lr=min_lr_basic+x*diff
+    return lr
 
 def printMarket(market):
     for i in range(len(market)):
@@ -273,37 +292,15 @@ runname="run_{}".format(run_number)
 model_db=[None,None] # object for storing model
 
 
-model = MaskablePPO("MlpPolicy",n_steps=int(env_config['episode_length']*2),ent_coef=0.1, vf_coef=0.5 ,gamma=0.99, learning_rate=1e-5,env=stackenv_basic, seed=300,verbose=1,device="cuda",tensorboard_log="./log")
-model_trade=MaskablePPO("MlpPolicy",n_steps=int(env_config['episode_length']*2),ent_coef=0.1, vf_coef=0.5 ,gamma=0.99, learning_rate=1e-5,env=stackenv_traid, seed=300,verbose=1,device="cuda",tensorboard_log="./log")
+model = MaskablePPO("MlpPolicy",n_steps=int(env_config['episode_length']*2),ent_coef=0.1, vf_coef=0.5 ,gamma=0.99, learning_rate=learning_rate_adj_basic,env=stackenv_basic, seed=445,verbose=1,device="cuda",tensorboard_log="./log")
+model_trade=MaskablePPO("MlpPolicy",n_steps=int(env_config['episode_length']*2),ent_coef=0.1, vf_coef=0.5 ,gamma=0.99, learning_rate=learning_rate_adj_trade,env=stackenv_traid, seed=445,verbose=1,device="cuda",tensorboard_log="./log")
+# Setup complete
 
-n_agents=econ.n_agents
-
-total_required_for_episode_basic=len(mobileRecieverEconWrapper.agnet_idx)*env_config['episode_length']
-total_required_for_episode_traid=len(tradeRecieverEconWrapper.agnet_idx)*env_config['episode_length']
-
-print("this is run {}".format(runname))
+# Load models
+model.load("basic.ai")
+model_trade.load("trade.ai")
 
 while True:
- 
-
-    #Train
-    runname="run_{}_{}".format(run_number,"basic")
-
-    thread_model=Thread(target=train,args=(model,total_required_for_episode_basic*50,econ,True,runname,model_db,0))
-    runname="run_{}_{}".format(run_number,"trader")
-    thread_model_traid=Thread(target=train,args=(model_trade,total_required_for_episode_traid*50,econ,False,runname,model_db,1))
-    
-    thread_model.start()
-    thread_model_traid.start()
-    thread_model.join()
-    thread_model_traid.join()
-    #normenv.save("temp-normalizer.ai")
-    model=model_db[0]
-    model_trade=model_db[1]
-    model.save("basic.ai")
-    model_trade.save("trade.ai")
-   
-    ## Run Eval
     print("### EVAL ###")
     obs_basic=stackenv_basic_eval.reset()
     obs_trade=stackenv_traid_eval.reset()
@@ -329,8 +326,8 @@ while True:
     craft=econ_eval.get_component("Craft")
    # trades=market.get_dense_log()
     build=craft.get_dense_log()
-    met=econ.previous_episode_metrics
-    printReplay(econ_eval,0)
+    met=econ_eval.previous_episode_metrics
+    printReplay(econ_eval,21)
    # printMarket(trades)
   #  printBuilds(builds=build)
     print("social/productivity: {}".format(met["social/productivity"]))