from ai_economist import foundation import numpy as np from stable_baselines3.common.vec_env import vec_frame_stack from stable_baselines3.common.evaluation import evaluate_policy import envs from tqdm import tqdm import components from stable_baselines3.common.env_checker import check_env from stable_baselines3 import PPO from stable_baselines3.common.vec_env.vec_monitor import VecMonitor from stable_baselines3.common.vec_env.vec_normalize import VecNormalize from sb3_contrib import RecurrentPPO from envs.econ_wrapper import EconVecEnv from stable_baselines3.common.callbacks import BaseCallback import yaml import time 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', # ===== COMPONENTS ===== # Which components to use (specified as list of ("component_name", {component_kwargs}) tuples). # "component_name" refers to the Component class's name in the Component Registry (foundation.components) # {component_kwargs} is a dictionary of kwargs passed to the Component class # The order in which components reset, step, and generate obs follows their listed order below. 'components': [ # (1) Building houses ('SimpleCraft', {'skill_dist': "none", 'payment_max_skill_multiplier': 3}), # (2) Trading collectible resources #('ContinuousDoubleAuction', {'max_num_orders': 10}), # (3) Movement and resource collection ('SimpleGather', {}), ], # ===== SCENARIO CLASS ARGUMENTS ===== # (optional) kwargs that are added by the Scenario class (i.e. not defined in BaseEnvironment) 'starting_agent_coin': 0, 'fixed_four_skill_and_loc': True, # ===== STANDARD ARGUMENTS ====== # kwargs that are used by every Scenario class (i.e. defined in BaseEnvironment) 'n_agents': 20, # Number of non-planner agents (must be > 1) 'world_size': [1, 1], # [Height, Width] of the env world 'episode_length': 256, # Number of timesteps per episode 'allow_observation_scaling': True, 'dense_log_frequency': 100, 'world_dense_log_frequency':1, 'energy_cost':0, 'energy_warmup_method': "auto", 'energy_warmup_constant': 0, # In multi-action-mode, the policy selects an action for each action subspace (defined in component code). # Otherwise, the policy selects only 1 action. 'multi_action_mode_agents': False, 'multi_action_mode_planner': False, # When flattening observations, concatenate scalar & vector observations before output. # Otherwise, return observations with minimal processing. 'flatten_observations': False, # When Flattening masks, concatenate each action subspace mask into a single array. # Note: flatten_masks = True is required for masking action logits in the code below. 'flatten_masks': False, } 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', # ===== COMPONENTS ===== # Which components to use (specified as list of ("component_name", {component_kwargs}) tuples). # "component_name" refers to the Component class's name in the Component Registry (foundation.components) # {component_kwargs} is a dictionary of kwargs passed to the Component class # The order in which components reset, step, and generate obs follows their listed order below. 'components': [ # (1) Building houses ('SimpleCraft', {'skill_dist': "none", 'payment_max_skill_multiplier': 3}), # (2) Trading collectible resources #('ContinuousDoubleAuction', {'max_num_orders': 10}), # (3) Movement and resource collection ('SimpleGather', {}), ], # ===== SCENARIO CLASS ARGUMENTS ===== # (optional) kwargs that are added by the Scenario class (i.e. not defined in BaseEnvironment) 'starting_agent_coin': 0, 'fixed_four_skill_and_loc': True, # ===== STANDARD ARGUMENTS ====== # kwargs that are used by every Scenario class (i.e. defined in BaseEnvironment) 'n_agents': 20, # Number of non-planner agents (must be > 1) 'world_size': [1, 1], # [Height, Width] of the env world 'episode_length': 100, # Number of timesteps per episode 'allow_observation_scaling': True, 'dense_log_frequency': 10, 'world_dense_log_frequency':1, 'energy_cost':0, 'energy_warmup_method': "auto", 'energy_warmup_constant': 0, # In multi-action-mode, the policy selects an action for each action subspace (defined in component code). # Otherwise, the policy selects only 1 action. 'multi_action_mode_agents': False, 'multi_action_mode_planner': False, # When flattening observations, concatenate scalar & vector observations before output. # Otherwise, return observations with minimal processing. 'flatten_observations': False, # When Flattening masks, concatenate each action subspace mask into a single array. # Note: flatten_masks = True is required for masking action logits in the code below. 'flatten_masks': False, } num_frames=2 class TensorboardCallback(BaseCallback): """ Custom callback for plotting additional values in tensorboard. """ def __init__(self,econ, verbose=0): super().__init__(verbose) self.econ=econ self.metrics=econ.scenario_metrics() def _on_step(self) -> bool: # Log scalar value (here a random variable) prev_metrics=self.metrics if self.econ.previous_episode_metrics is None: self.metrics=self.econ.scenario_metrics() else: self.metrics=self.econ.previous_episode_metrics curr_prod=self.metrics["social/productivity"] trend_pord=curr_prod-prev_metrics["social/productivity"] self.logger.record("social/total_productivity", curr_prod) self.logger.record("social/delta_productivity", trend_pord) return True def sample_random_action(agent, mask): """Sample random UNMASKED action(s) for agent.""" # Return a list of actions: 1 for each action subspace if agent.multi_action_mode: split_masks = np.split(mask, agent.action_spaces.cumsum()[:-1]) return [np.random.choice(np.arange(len(m_)), p=m_/m_.sum()) for m_ in split_masks] # Return a single action else: return np.random.choice(np.arange(agent.action_spaces), p=mask/mask.sum()) def sample_random_actions(env, obs): """Samples random UNMASKED actions for each agent in obs.""" actions = { a_idx: 0 for a_idx in range( len(obs)) } return actions def printMarket(market): for i in range(len(market)): step=market[i] if len(step)>0: print("=== Step {} ===".format(i)) for transaction in step: t=transaction transstring = "({}) {} -> {} | [{}/{}] {} Coins\n".format(t["commodity"],t["seller"],t["buyer"],t["ask"],t["bid"],t["price"]) print(transstring) return "" def printBuilds(builds): for i in range(len(builds)): step=builds[i] if len(step)>0: for build in step: t=build transstring = "({}) Builder: {}, Skill: {}, Income {} ".format(i,t["builder"],t["build_skill"],t["income"]) print(transstring) return "" def printReplay(econ,agentid): worldmaps=["Stone","Wood"] log=econ.previous_episode_dense_log agent=econ.world.agents[agentid] agentid=str(agentid) maxsetp=len(log["states"])-1 for step in range(maxsetp): print() print("=== Step {} ===".format(step)) # state print("--- World ---") world=log['world'][step] for res in worldmaps: print("{}: {}".format(res,world[res][0][0])) print("--- State ---") state=log['states'][step][agentid] print(yaml.dump(state)) print("--- Action ---") action=log["actions"][step][agentid] if action=={}: print("Action: 0 -> NOOP") else: for k in action: formats="Action: {}({})".format(k,action[k]) print(formats) print("--- Reward ---") reward=log["rewards"][step][agentid] print("Reward: {}".format(reward)) #Setup Env Objects vecenv=EconVecEnv(env_config=env_config) econ=vecenv.env 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() 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'] print("this is run {}".format(runname)) 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 #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") ## 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'])): action=model.predict(obs) obs,rew,done_e,info=vec_mon_eval.step(action[0]) done=done_e[0] #market=eval_econ.get_component("ContinuousDoubleAuction") craft=eval_econ.get_component("SimpleCraft") # trades=market.get_dense_log() build=craft.get_dense_log() met=econ.previous_episode_metrics printReplay(eval_econ,0) # printMarket(trades) printBuilds(builds=build) print("social/productivity: {}".format(met["social/productivity"])) print("labor/weighted_cost: {}".format(met["labor/weighted_cost"])) print("labor/warmup_integrator: {}".format(met["labor/warmup_integrator"])) time.sleep(1)