def create_agent_66_experiment_runner(num_dcs, num_customers, dcs_per_customer,
demand_mean, demand_var, num_commodities,
orders_per_day, num_steps):
physical_network = PhysicalNetwork(
num_dcs,
num_customers,
dcs_per_customer,
demand_mean,
demand_var,
num_commodities,
)
order_generator = ActualOrderGenerator(physical_network, orders_per_day)
generator = DirichletInventoryGenerator(physical_network)
environment_parameters = EnvironmentParameters(physical_network,
order_generator, generator,
num_steps)
env = ShippingFacilityEnvironment(environment_parameters)
agent = AgentHighest(env)
return ExperimentRunner(order_generator,
generator,
agent,
env,
experiment_name="randomvalid_validation")
def run_with_params(num_dcs, num_customers, dcs_per_customer, demand_mean,
demand_var, num_commodities, orders_per_day, num_steps):
physical_network = PhysicalNetwork(
num_dcs,
num_customers,
dcs_per_customer,
demand_mean,
demand_var,
num_commodities,
)
# order_generator = NaiveOrderGenerator(num_dcs, num_customers, orders_per_day)
order_generator = ActualOrderGenerator(physical_network, orders_per_day)
generator = DirichletInventoryGenerator(physical_network)
environment_parameters = EnvironmentParameters(physical_network,
order_generator, generator,
num_steps)
env = ShippingFacilityEnvironment(environment_parameters)
agent = RandomAgent(env)
obs = env.reset()
reward = 0
done = False
print("=========== starting episode loop ===========")
print("Initial environment: ")
env.render()
actions = []
episode_rewards = []
#demands_per_k = np.zeros((num_commodities,num_steps))
#inventory_at_t = np.zeros((num_commodities,num_steps)) #todo llenar estos eventualmente
while not done:
action = agent.act(obs, reward, done)
# print(f"Agent is taking action: {action}")
# the agent observes the first state and chooses an action
# environment steps with the agent's action and returns new state and reward
obs, reward, done, info = env.step(action)
# print(f"Got reward {reward} done {done}")
# Render the current state of the environment
env.render()
actions.append(action)
episode_rewards.append(reward)
if done:
print("===========Environment says we are DONE ===========")
return actions, episode_rewards
def main():
config_dict = {
"env": {
"num_dcs": 4, #3
"num_customers": 100,
"num_commodities": 35,
"orders_per_day": int(100 * 0.05),
"dcs_per_customer": 2,
"demand_mean": 500,
"demand_var": 150,
"num_steps": 30, # steps per episode
"big_m_factor":
10000 # how many times the customer cost is the big m.
},
"hps": {
"env": "shipping-v0", #openai env ID.
"episode_length": 30,
"max_episodes":
35, # to do is this num episodes, is it being used?
"lr": 1e-4,
"discount": 0.95,
"epsilon": 0.01,
# "batch_size": 30,
# "sync_rate": 2, # Rate to sync the target and learning network.
},
"seed": 0,
"agent": "q_learning"
# "agent": "random_valid"
}
torch.manual_seed(config_dict['seed'])
np.random.seed(config_dict['seed'])
random.seed(config_dict['seed']) # not sure if actually used
np.random.seed(config_dict['seed'])
run = wandb.init(config=config_dict) # todo why not saving config???
config = wandb.config
environment_config = config.env
hparams = config.hps
experiment_name = f"q_{config.agent}_few_warehouses"
wandb_logger = WandbLogger(
project="rl_warehouse_assignment",
name=experiment_name,
tags=[
"debug"
# "experiment"
],
log_model=False)
wandb_logger.log_hyperparams(dict(config))
environment_parameters = network_flow_env_builder.build_network_flow_env_parameters(
environment_config, hparams['episode_length'], order_gen='biased')
env = ShippingFacilityEnvironment(environment_parameters)
agent = q_learning_agent.ShippingEnvQLearningAgent(
environment_config['num_customers'], environment_config['num_dcs'],
hparams['lr'], hparams['discount'], hparams['epsilon'], env)
model = GreedyAgentRLModel(agent, env, experiment_name=experiment_name)
trainer = pl.Trainer(
max_epochs=hparams['max_episodes'],
early_stop_callback=False,
val_check_interval=100,
logger=wandb_logger,
log_save_interval=1,
row_log_interval=1, # the default of this may leave info behind.
callbacks=[
MyPrintingCallback(),
ShippingFacilityEnvironmentStorageCallback(
experiment_name,
base="data/results/",
experiment_uploader=WandbDataUploader())
])
trainer.fit(model)