def __init__(self, config_file_name, seed=0, verbose=False):
self.verbose = verbose
self.env = blackbox.EnvBlackBox(seed)
#print environment info
if (verbose):
self.env.print_info()
#init DQN agent
self.agent = agent_dqn.DQNAgent(self.env, config_file_name, 0.3, 0.05,
0.99999)
#self.agent = agent.Agent(self.env)
#iterations count
self.training_iterations = 100000
self.testing_iterations = 10000
def __init__(self, seed=0, verbose=False):
self.verbose = verbose
self.env = blackbox.EnvBlackBox(seed)
#print environment info
if (verbose):
self.env.print_info()
#init DQN agent
self.agent = dqn.DQNAgent(
self.env, "networks/black_box_network/net_0_parameters.json", 0.3,
0.05, 0.99999)
#iterations count
self.training_iterations = 100000
self.testing_iterations = 10000
def __init__(self,
bot_file_name,
training_iterations,
testing_iterations,
seed=0,
verbose=False,
render=False):
self.verbose = verbose
self.render = render
self.env = blackbox.EnvBlackBox(seed)
#print environment info
if (verbose):
self.env.print_info()
print("loading agent ", bot_file_name)
#init DQN agent
self.agent = libs.libs_agent.agent_dqn.DQNAgent(
self.env, bot_file_name, 0.3, 0.05, 0.99999)
#iterations count
self.training_iterations = training_iterations
self.testing_iterations = testing_iterations
import sys
sys.path.append("..") # Adds higher directory to python modules path.
import libs.libs_env.blackbox.env_black_box as env_black_box
import libs.libs_agent.agent_dqn
#init environment
env = env_black_box.EnvBlackBox(4)
#print environment info
env.print_info()
gamma = 0.95
replay_buffer_size = 256
epsilon_training = 1.0
epsilon_testing = 0.05
epsilon_decay = 0.9999
agent = libs.libs_agent.agent_dqn.DQNAgent(
env, "networks/black_box_network/network_config.json", gamma,
replay_buffer_size, epsilon_training, epsilon_testing, epsilon_decay)
#process training
training_iterations = 100000
for i in range(0, training_iterations):
agent.main()
if env.get_iterations() % 256 == 0:
print(" iterations = ", env.get_iterations(), " score = ",