env, "networks/settlers_network/parameters.json", 0.2, 0.1) #0.2, 0.1
#agent = libs_agent.agent.Agent(env)
#process training
training_iterations = 500000
for iteration in range(0, training_iterations):
agent.main()
#print training progress %, ane score, every 100th iterations
if iteration % 100 == 0:
env._print()
print(iteration * 100.0 / training_iterations, env.get_score())
agent.save("networks/settlers_network/trained/")
agent.load("networks/settlers_network/trained/")
#reset score
env.reset_score()
#choose only the best action
agent.run_best_enable()
#process testing iterations
testing_iterations = 10000
for iteration in range(0, testing_iterations):
agent.main()
print("move=", env.get_move(), " score=", env.get_score(),
" moves to win=", env.get_moves_to_win())
while True:
#print environment info
env.print_info()
#init DQN agent
gamma = 0.99
replay_buffer_size = 16384
epsilon_training = 1.0
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)
'''
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:
#init DQN agent
agent = libs.libs_agent.agent_dqn.DQNAgent(
env, "networks/arkanoid_network_b/parameters.json", 0.2, 0.02, 0.99999)
#process training
training_iterations = 250000
for iteration in range(0, training_iterations):
agent.main()
#print training progress %, ane score, every 100th iterations
if iteration % 100 == 0:
env._print()
agent.save("networks/arkanoid_network_b/trained/")
agent.load("networks/arkanoid_network_b/trained/")
#reset score
env.reset_score()
#choose only the best action
agent.run_best_enable()
#process testing iterations
testing_iterations = 10000
for iteration in range(0, testing_iterations):
agent.main()
env._print()
while True:
agent.main()