manuel/ai-econ
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

its not braking anymore

Browse Source
This commit is contained in:
Manuel Plonski
2023-01-14 21:26:04 +01:00
parent 166c96e66d
commit fcd25276bb
4 changed files with 54 additions and 25 deletions
Download Patch File Download Diff File Expand all files Collapse all files

26
components/crafting.py
View File

@@ -50,6 +50,7 @@ class Craft(BaseComponent):
assert len(commodities)>0
#setup commodities
self.recip_map={}
self.commodities=[]
for v in commodities:
res_class=resource_registry.get(v)
res=res_class()
@@ -80,11 +81,14 @@ class Craft(BaseComponent):
def agent_can_build(self, agent, res):
"""Return True if agent can actually build in its current location."""
# See if the agent has the resources necessary to complete the action
recipe= self.recip_map[res]
for resource, cost in recipe.items():
if agent.state["inventory"][resource] < cost:
return False
return True
if res in self.recip_map:
recipe= self.recip_map[res]
for resource, cost in recipe.items():
if agent.state["inventory"][resource] < cost:
return False
else:
return True
return False
# Required methods for implementing components
# --------------------------------------------
@@ -136,9 +140,10 @@ class Craft(BaseComponent):
# Build! (If you can.)
else:
action-=1
comm=self.commodities[action]
if self.agent_can_build(agent,comm.craft_recp):
if self.agent_can_build(agent,comm.name):
# Remove the resources
for resource, cost in comm.craft_recp.items():
agent.state["inventory"][resource] -= cost
@@ -211,7 +216,7 @@ class Craft(BaseComponent):
where metric_value is a scalar.
"""
world = self.world
"""
build_stats = {a.idx: {"n_builds": 0} for a in world.agents}
for builds in self.builds:
for build in builds:
@@ -225,8 +230,8 @@ class Craft(BaseComponent):
num_houses = np.sum(world.maps.get("House") > 0)
out_dict["total_builds"] = num_houses
return out_dict
"""
return {}
def additional_reset_steps(self):
"""
@@ -257,7 +262,8 @@ class Craft(BaseComponent):
elif self.skill_dist == "pareto":
labour = 1
sampled_skill = np.random.pareto(2)
amount = np.minimum(MSAB, MSAB * sampled_skill)
amount = 1+np.minimum(MSAB,(MSAB-1) * (sampled_skill) )
labour_modifier = 1 - np.minimum(1 - MSLB, (1 - MSLB) * sampled_skill)
else:
raise NotImplementedError

19
components/external_market.py
View File

@@ -107,8 +107,8 @@ class ExternalMarket(BaseComponent):
# NO-OP!
if action == 0:
pass
action-=1
continue
res_name=self.action_res_map[action]
# Build! (If you can.)
@@ -163,10 +163,11 @@ class ExternalMarket(BaseComponent):
# Mobile agents' build action is masked if they cannot build with their
# current location and/or endowment
for agent in self.world.agents:
mask=[]
for res in self.market_demand:
mask.append(self.agent_can_sell(agent,res))
masks[agent.idx] = mask
if agent.name in self.agent_subclasses:
mask=[]
for res in self.market_demand:
mask.append(self.agent_can_sell(agent,res))
masks[agent.idx] = mask
return masks
@@ -182,7 +183,7 @@ class ExternalMarket(BaseComponent):
where metric_value is a scalar.
"""
world = self.world
"""
build_stats = {a.idx: {"n_builds": 0} for a in world.agents}
for builds in self.builds:
for build in builds:
@@ -196,8 +197,8 @@ class ExternalMarket(BaseComponent):
num_houses = np.sum(world.maps.get("House") > 0)
out_dict["total_builds"] = num_houses
return out_dict
"""
return {}
def additional_reset_steps(self):
"""

31
main.py
View File

@@ -23,6 +23,7 @@ from envs.econ_wrapper import EconVecEnv
from stable_baselines3.common.callbacks import BaseCallback
import yaml
import time
from threading import Thread
env_config = {
# ===== SCENARIO CLASS =====
@@ -61,7 +62,7 @@ env_config = {
'allow_observation_scaling': True,
'dense_log_frequency': 100,
'world_dense_log_frequency':1,
'energy_cost':0.21,
'energy_cost':0,
'energy_warmup_method': "auto",
'energy_warmup_constant': 4000,
@@ -116,7 +117,7 @@ eval_env_config = {
'allow_observation_scaling': True,
'dense_log_frequency': 10,
'world_dense_log_frequency':1,
'energy_cost':0.21,
'energy_cost':0,
'energy_warmup_method': "auto",
'energy_warmup_constant': 4000,
@@ -223,23 +224,33 @@ baseEconWrapper=BaseEconWrapper(econ)
baseEconWrapper.run()
mobileRecieverEconWrapper=RecieverEconWrapper(base_econ=baseEconWrapper,agent_classname="BasicMobileAgent")
tradeRecieverEconWrapper=RecieverEconWrapper(base_econ=baseEconWrapper,agent_classname="TradingAgent")
sb3_traderConverter=SB3EconConverter(tradeRecieverEconWrapper,econ,"TradingAgent")
sb3Converter=SB3EconConverter(mobileRecieverEconWrapper,econ,"BasicMobileAgent")
#obs=sb3Converter.reset()
#vecenv=EconVecEnv(env_config=env_config)
monenv=VecMonitor(venv=sb3Converter,info_keywords=["social/productivity","trend/productivity"])
montraidingenv=VecMonitor(venv=sb3_traderConverter)
#normenv=VecNormalize(sb3Converter,norm_reward=False,clip_obs=1)
#stackenv=vec_frame_stack.VecFrameStack(venv=monenv,n_stack=10)
obs=monenv.reset()
# define training functions
def train(model,timesteps, econ_call,process_bar,name,db,index):
db[index]=model.learn(total_timesteps=timesteps,progress_bar=process_bar,reset_num_timesteps=False,tb_log_name=name,callback=TensorboardCallback(econ_call))
runname="run_{}".format(int(np.random.rand()*100))
# prepare training
run_number=int(np.random.rand()*100)
runname="run_{}".format(run_number)
model_db=[] # object for storing model
model = MaskablePPO("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, seed=225,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=0.8 ,gamma=0.95, learning_rate=5e-3,env=montraidingenv, seed=225,verbose=1,device="cuda",tensorboard_log="./log")
n_agents=econ.n_agents
total_required_for_episode=n_agents*env_config['episode_length']
print("this is run {}".format(runname))
@@ -249,11 +260,21 @@ 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_mon_eval=VecMonitor(venv=eval_sb3_converter)
#Train
model=model.learn(total_timesteps=total_required_for_episode*10,progress_bar=True,reset_num_timesteps=False,tb_log_name=runname,callback=TensorboardCallback(econ=econ))
runname="run_{}_{}".format(run_number,"basic")
thread_model=Thread(target=train,args=(model,total_required_for_episode*10,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*10,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")

3
wrapper/base_econ_wrapper.py
View File

@@ -149,7 +149,8 @@ class BaseEconWrapper():
self.action_edit_lock.acquire() # Start to submit action dict
for k,v in actions.items():
if k in self.actor_actions.keys():
raise Exception("Actor action has already been submitted. {}".format(k))
print("Actor action has already been submitted. {}".format(k))
continue
self.actor_actions[k]=v
self.step_notifications[voter_id].clear()
self.base_notification.set() #Alert base for action changes