def run_learnt_greedy(saveJson=True):
Globals().cars_out_memory = []
model_file_names = [
'static_files/model-agent0.h5', 'static_files/model-agent1.h5',
'static_files/model-agent2.h5', 'static_files/model-agent3.h5'
]
agents = get_LearnSmartAgents(model_file_names)
env = Env(agents)
u = epoch_greedy(env)
rewards_sum, rewards_mean = count_rewards(env)
cars_out = env.cars_out
if saveJson:
exportData = ExportData(learningMethod='DQN',
learningEpochs=0,
nets=env.global_memories,
netName='env4',
densityName='learnt_' +
str(Globals().greedy_run_no))
exportData.saveToJson()
maximum_possible_cars_out = Globals().u_value * Globals(
).vp.max_time_greedy * 8
cars_out_percentage = round(100 * cars_out / maximum_possible_cars_out, 2)
print(
f'gready run {Globals().greedy_run_no} - rewards_mean:{round(rewards_mean, 2)} rewar'
f'ds_sum:{round(rewards_sum, 0)}. Do układu wjechało {round(sum(sum(u)), 0)} pojazdów.'
f' Wyjechało {round(cars_out, 0)}. Układ opuściło pr'
f'ocentowo pojazdów:{cars_out_percentage}')
Globals().greedy_run_no += 1
return rewards_mean, rewards_sum, cars_out, agents, sum(
sum(u)), cars_out_percentage
def run_learnt_greedy(saveJson=False):
Globals().cars_out_memory = []
Globals().cars_in_memory = []
saveJson = True
model_file_names = [
'static_files/model-agent0.h5', 'static_files/model-agent1.h5',
'static_files/model-agent2.h5', 'static_files/model-agent3.h5'
]
agents = get_LearnSmartAgents(model_file_names)
# print('weights!',agents[0].model.weights[0])
env = Env(agents)
epoch_greedy(env)
# env.update_memory_rewards() # TODO czy to mozna odkomentowac?
rewards_sum, rewards_mean = count_rewards(env)
cars_out = env.cars_out
print('cars_out', cars_out)
if saveJson:
exportData = ExportData(learningMethod='DQN',
learningEpochs=0,
nets=env.global_memories,
netName='polibuda',
densityName='learnt_' +
str(Globals().greedy_run_no))
exportData.saveToJson()
# print('exported')
maximum_possible_cars_out = Globals().u_value * Globals().vp(
).max_time_greedy * 8
print('memory losowych', Globals().actions_memory)
print('max greedy', max([max(x) for x in env.x]))
print(
f'gready run {Globals().greedy_run_no} - rewards_mean:{rewards_mean} rewards_sum:{rewards_sum} cars_out:{cars_out} procentowo:{float(cars_out)/maximum_possible_cars_out}'
)
Globals().greedy_run_no += 1
return rewards_mean, rewards_sum, cars_out, agents
def run_learnt_greedy(saveJson=False):
# saveJson = True
model_file_names = ['static_files/model-agent0.h5']
agents = get_LearnSmartAgents(model_file_names)
# print('weights!',agents[0].model.weights[0])
env = Env(agents)
epoch_greedy(env)
# env.update_memory_rewards() # TODO czy to mozna odkomentowac?
rewards_sum, rewards_mean = count_rewards(env)
cars_out = env.cars_out
if saveJson:
exportData = ExportData(learningMethod='DQN',
learningEpochs=0,
nets=env.global_memories,
netName='net11',
densityName='learnt_' +
str(Globals().greedy_run_no))
exportData.saveToJson()
Globals().greedy_run_no += 1
# print(f'gready run - rewards_mean:{rewards_mean} rewards_sum:{rewards_sum} cars_out:{cars_out}')
return rewards_mean, rewards_sum, cars_out
def run_learnt_greedy(saveJson=False):
model_file_names = [
'static_files/model-agent0.h5', 'static_files/model-agent1.h5',
'static_files/model-agent2.h5'
]
agents = get_LearnSmartAgents(model_file_names)
env = Env(agents)
epoch_greedy(env)
env.update_memory_rewards()
rewards_sum, rewards_mean = count_rewards(env)
cars_out = env.cars_out
if saveJson:
exportData = ExportData(learningMethod='DQN',
learningEpochs=0,
nets=env.global_memories,
netName='net4',
densityName='learnt-' +
str(Globals().greedy_run_no))
exportData.saveToJson()
Globals().greedy_run_no += 1
print(
f'rewards_mean:{rewards_mean} rewards_sum:{rewards_sum} cars_out:{cars_out}'
)
return rewards_mean, rewards_sum, cars_out