class LEARNER():
def __init__(self, args, sess, simulator):
self.args = args
self.sess = sess
self.simulator = simulator
#Construct simulation environment
self.simulator = gym.make('Pendulum-v0')
#Define learning agent (TRPO)
self.agent = TRPO(self.args, self.simulator, self.sess)
def learn(self):
train_index = 0
total_episode = 0
total_steps = 0
all_logs = list()
while True:
#Train the TRPO agent
train_index += 1
train_log = self.agent.train()
total_steps += train_log["Total Step"]
total_episode += train_log["Num episode"]
all_logs.append(train_log)
#Simulate system w/ new parameters
if train_index % 20 == 0:
self.agent.sim()
if total_steps > self.args.total_train_step:
nn_weights = {
'policy_network': self.agent.get_value(),
'advantage_network': self.agent.gae.get_value()
}
savemat(
'data_' +
datetime.datetime.now().strftime("%y-%m-%d-%H-%M") +
'.mat', dict(data=all_logs, args=self.args))
savemat(
'weights_' +
datetime.datetime.now().strftime("%y-%m-%d-%H-%M") +
'.mat', dict(policy_weights=nn_weights, args=self.args))
break
class LEARNER():
def __init__(self, args, sess, simulator):
self.args = args
self.sess = sess
self.simulator = simulator
#Construct simulation environment
self.simulator = gym.make('Pendulum-v0')
self.simulator.unwrapped.max_torque = 15.
self.simulator.unwrapped.max_speed = 60.
self.simulator.unwrapped.action_space = spaces.Box(low=-self.simulator.unwrapped.max_torque, high=self.simulator.unwrapped.max_torque, shape=(1,))
high = np.array([1., 1., self.simulator.unwrapped.max_speed])
self.simulator.unwrapped.observation_space = spaces.Box(low=-high, high=high)
#Define learning agent (TRPO)
self.agent = TRPO(self.args, self.simulator, self.sess)
def learn(self):
train_index = 0
total_episode = 0
total_steps = 0
all_logs = list()
while True:
#Train the TRPO agent
train_index += 1
train_log = self.agent.train()
total_steps += train_log["Total Step"]
total_episode += train_log["Num episode"]
all_logs.append(train_log)
#Simulate system w/ new parameters
if train_index%5 == 0:
self.agent.sim()
print(train_index)
if total_steps > self.args.total_train_step:
savemat('data4_' + datetime.datetime.now().strftime("%y-%m-%d-%H-%M") + '.mat',dict(data=all_logs, args=self.args))
break