def __init__(self, net_type, conf):
if net_type == 'drqn':
self.agent = DRQNAgent(conf)
elif net_type == 'gru':
self.agent = GRUAgent(conf)
else:
self.agent = DQNAgent(conf)
def __init__(self, net_type, conf):
if net_type == "drqn":
self.agent = DRQNAgent(conf)
elif net_type == "dgruqn":
self.agent = DGRUQNAgent(conf)
else:
self.agent = DQNAgent(conf)
class Main():
def __init__(self, net_type, conf):
self.agent = DQNAgent(conf)
#Se encarga de realizar todo el proceso de entrenamiento del agente
def train(self, steps):
self.agent.train(steps)
#Se encarga de jugar un episodio con lo aprendido por la red neuronal especificiada.
def play(self, steps, net_path):
self.agent.play(steps, net_path)
class Main():
def __init__(self, net_type, conf):
if net_type == "drqn":
self.agent = DRQNAgent(conf)
else:
self.agent = DQNAgent(conf)
def train(self, steps):
self.agent.train(steps)
def play(self, episodes, net_path):
self.agent.play(episodes, net_path)
def __init__(self, network_type, conf):
if network_type == 'dqn':
print('DQN')
self.agent = DQNAgent(conf)
elif network_type == 'drqn':
print('LSTM-DRQN')
self.agent = DRQNAgent(conf)
elif network_type == 'goru':
print('GORU-DRQN')
self.agent = GORUAgent(conf)
elif network_type == 'eunn':
print('EUNN-DRQN')
self.agent = EUNNAgent(conf)
else:
raise ValueError('Incompatible network type ' + network_type)
def __init__(self, net_type, conf):
self.agent = DQNAgent(conf)
EPS_DECAY = 0.995
EPS_MIN = 0.01
def get_env_path():
if len(sys.argv) != 2:
print("ERROR: invalid arguments list")
print("Usage: train.py <path_to_unity_env>")
sys.exit(1)
return sys.argv[1]
if __name__ == '__main__':
env = BananaEnv(get_env_path())
agent = DQNAgent(
env,
DQNConfig(
buffer_size=BUFFER_SIZE,
batch_size=BATCH_SIZE,
learning_rate=LEARNING_RATE,
tau=TAU,
gamma=GAMMA,
fc1_units=FC1_UNITS,
fc2_units=FC2_UNITS,
episode_max_length=EPISODE_MAX_LENGTH,
))
agent.train(N_STEPS, UPDATE_EVERY, PRINT_EVERY, EPS_INIT, EPS_DECAY,
EPS_MIN)
input('Press key to continue...')
env.close()
import sys
from src.banana_env import BananaEnv
from src.dqn_agent import DQNAgent, DQNConfig
from src.random_agent import RandomAgent
# choose agent: dqn | random
AGENT = "dqn"
def get_env_path():
if len(sys.argv) != 2:
print("ERROR: invalid arguments list")
print("Usage: rollout.py <path_to_unity_env>")
sys.exit(1)
return sys.argv[1]
if __name__ == "__main__":
env = BananaEnv(get_env_path())
agent = RandomAgent(env) if AGENT == "random" else DQNAgent(
env, DQNConfig())
agent.restore("model.pt")
agent.run_episode()
env.close()