agent.load(network_path + "trained/")
agent.run_best_enable()
while True:
agent.main()
env._print()
'''
training_progress_log = rysy.Log(network_path + "progress_training.log")
testing_progress_log = rysy.Log(network_path + "progress_testing.log")
#process training
total_games_to_play = 20000
while env.get_games_count() < total_games_to_play:
agent.main()
#print training progress %, ane score, every 256th iterations
if verbose:
if env.get_iterations()%256 == 0:
env._print()
env.render()
if env.get_iterations()%256 == 0:
str_progress = str(env.get_iterations()) + " "
str_progress+= str(env.get_games_count()) + " "
str_progress+= str(agent.get_epsilon_training()) + " "
str_progress+= str(env.get_score()) + " "
str_progress+= "\n"
training_progress_log.put_string(str_progress)
epsilon_testing = 0.1
epsilon_decay = 0.99999
#init DQN agent
agent = libs.libs_agent.agent_dqn.DQNAgent(
env, network_path + "network_config.json", gamma, replay_buffer_size,
epsilon_training, epsilon_testing, epsilon_decay)
training_progress_log = rysy.Log(network_path + "progress_training.log")
testing_progress_log = rysy.Log(network_path + "progress_testing.log")
#process training
total_games_to_play = 30000
while env.get_games_count() < total_games_to_play:
result = agent.main()
if result != 0:
print("ERROR : agent returned ", result, "\n\n\n\n")
break
if env.get_iterations() % 256 == 0:
str_progress = str(env.get_iterations()) + " "
str_progress += str(env.get_games_count()) + " "
str_progress += str(agent.get_epsilon_training()) + " "
str_progress += str(env.get_score()) + " "
str_progress += str(env.get_kill_count()) + " "
str_progress += str(env.get_death_count()) + " "
str_progress += str(env.get_game_kd_ratio()) + " "
str_progress += str(env.get_kd_ratio()) + " "
str_progress += "\n"
training_progress_log.put_string(str_progress)
