def reset(self):
self.state = self.np_random.uniform(low=-0.05, high=0.05, size=(2,))
# print(str({"metric": "epoch_reward", "value": self.epoch_reward, "step": self.total_steps}))
print('{"metric": "epoch_reward", "value": '+str(self.epoch_reward)+', "step":'+str(self.total_steps)+'}')
logbook().record_epoch_reward(self.epoch_reward)
self.epoch_reward = 0
return np.array(self.state)
def reset(self):
# We used to reset the state each time - dont see how this is sensible as its a continuation in simulation backend.
# Still worthwhile having regular epochs as we want it to think about maximising daily bidding (or some other period)
# But resetting state is a little incompehensible.
# Edge case here is first instance, whereby state is set to obs_low.
# self.state = self.np_random.uniform(low=0, high=0.05, size=(8,))
# print(str({"metric": "epoch_reward", "value": self.epoch_reward, "step": self.total_steps}))
print('{"metric": "epoch_reward", "value": '+str(self.epoch_reward)+', "step":'+str(self.total_steps)+'}')
logbook().record_epoch_reward(self.epoch_reward)
self.epoch_reward = 0
return np.array(self.state)
def reset(self):
# We used to reset the state each time - dont see how this is sensible as its a continuation in simulation backend.
# Still worthwhile having regular epochs as we want it to think about maximising daily bidding (or some other period)
# But resetting state is a little incompehensible.
# Edge case here is first instance, whereby state is set to obs_low.
# self.state = self.np_random.uniform(low=0, high=0.05, size=(8,))
# print(str({"metric": "epoch_reward", "value": self.epoch_reward, "step": self.total_steps}))
print('{"metric": "epoch_reward", "value": ' + str(self.epoch_reward) +
', "step":' + str(self.total_steps) + '}')
print('{"metric": "unique_bids", "value": ' +
str(logbook().get_num_unique_bids(previous_steps=50)) +
', "step":' + str(self.total_steps) + '}')
logbook().record_epoch_reward(self.epoch_reward)
self.epoch_reward = 0
# Every X steps, write results to file in case of dramatic failure.
if self.total_steps % 20000 == 0:
# if self.total_steps % 100 == 0:
logbook().save_json(label=str(self.label))
return np.array(self.state)
def render(self, mode='human'):
# Log bid/value in Floydhub
# print('{"metric": "bid", "value": '+str(self.last_action)+', "step":'+str(self.total_steps)+'}')
# Log in logbook suite
logbook().record_price(self._state_dict['price'], self.total_steps)
logbook().record_demand(self._state_dict['demand'], self.total_steps)
# Log bidstack in logbook suite.
for bid in self._state_dict['all_bids']:
logbook().record_bid(bid['label'], bid['price'], bid['quantity'], self.total_steps)
return None
sys.exit()
# Make sure that the participant name is one of hte allowed ones.
elif sys.argv[1] not in market_config['PARTICIPANTS']:
print(
'Error: Participant not in list of possible participants. Must be one of:'
)
[print(" -" + p) for p in market_config['PARTICIPANTS']]
sys.exit()
else:
participant_name = sys.argv[1]
# ENV_NAME = 'CartPole-v0'
# ENV_NAME = 'MultiBidMarket-v0'
ENV_NAME = 'MultiBidMarketEfficient-v0'
logbook().record_metadata('Environment', ENV_NAME)
logbook().record_metadata('datetime', pendulum.now().isoformat())
for param in market_config:
logbook().record_metadata('Market: ' + param, market_config[param])
# # Set the tensorflow memory growth to auto - this is important when running two simultaneous models
# # Otherwise, the first process hogs all the memory and the second (the one that we watch the output of)
# # gets a CUDA_ERROR_OUT_OF_MEMORY message and crashes. Instead ofthe majority of below, you can also use config.gpu_options.allow_growth = True #Set automatically - takes some time.
# config = tf.ConfigProto()
# config.gpu_options.per_process_gpu_memory_fraction = 0.95 / float(len(market_config['PARTICIPANTS'])) # Alternatively, allocate as a fraction of the available memory:
# sess = tf.Session(config=config)
# K.set_session(sess)
# Get the environment and extract the number of actions.
env = gym.make(ENV_NAME)
# Wrap so that we have a discrete action space - maps the internal MultiDiscrete action space to a Discrete action space.
from marketsim.logbook.logbook import logbook
import pendulum
logbook().set_label("TEST" + " " + pendulum.now().format('ddd D/M HH:mm'))
logbook().record_hyperparameter('alpha', 1)
logbook().record_hyperparameter('beta', 2)
logbook().submit()
