def train_prioritized(Q_Network, train_batch, w_batch, Exp, s_scale,
input_size, num_actions):
state_t_batch = [item[0] for item in train_batch]
state_t_batch = np.array(state_t_batch)
state_t_1_batch = [item[1] for item in train_batch]
state_t_1_batch = np.array(state_t_1_batch)
action_batch = [item[2] for item in train_batch]
reward_batch = [item[3] for item in train_batch]
reward_batch = np.array(reward_batch)
done_batch = [item[4] for item in train_batch]
done_batch = np.array(done_batch)
action_t_batch = Q_Network.evaluate_critic(state_t_batch)
w_batch = np.transpose(np.tile(w_batch, (num_actions, 1)))
if Exp == 'epsilon':
action_t_1_batch = Q_Network.evaluate_critic(state_t_1_batch)
q_t_1 = Q_Network.evaluate_target_critic(state_t_1_batch)
for i in range(0, len(train_batch)):
if done_batch[i]:
action_t_batch[i][action_batch[i]] = reward_batch[i]
else:
action_t_batch[i][action_batch[i]] = reward_batch[
i] + GAMMA * q_t_1[i][np.argmax(action_t_1_batch[i])]
elif Exp == 'softmax':
action_t_1_batch = Q_Network.evaluate_target_critic(state_t_1_batch)
q_t_1 = Exploration.softV(action_t_1_batch, s_scale)
for i in range(0, len(train_batch)):
if done_batch[i]:
action_t_batch[i][action_batch[i]] = reward_batch[i]
else:
action_t_batch[i][
action_batch[i]] = reward_batch[i] + GAMMA * q_t_1[i]
elif Exp == 'sparsemax':
action_t_1_batch = Q_Network.evaluate_target_critic(state_t_1_batch)
q_t_1 = Exploration.sparsemax(action_t_1_batch, s_scale)
for i in range(0, len(train_batch)):
if done_batch[i]:
action_t_batch[i][action_batch[i]] = reward_batch[i]
else:
action_t_batch[i][
action_batch[i]] = reward_batch[i] + GAMMA * q_t_1[i]
#Q_Network.train_critic_prioritized(state_t_batch, action_t_batch, w_batch)
errors, cost, _ = Q_Network.train_critic_prioritized(
state_t_batch, action_t_batch, w_batch)
errors = np.sum(errors, axis=1)
return errors, cost
def train(Q_Network, train_batch, Exp, s_scale, input_size, num_actions):
state_t_batch = [item[0] for item in train_batch]
state_t_batch = np.array(state_t_batch)
state_t_1_batch = [item[1] for item in train_batch]
state_t_1_batch = np.array(state_t_1_batch)
action_batch = [item[2] for item in train_batch]
reward_batch = [item[3] for item in train_batch]
reward_batch = np.array(reward_batch)
done_batch = [item[4] for item in train_batch]
done_batch = np.array(done_batch)
action_t_batch = Q_Network.evaluate_critic(state_t_batch)
if Exp == 'epsilon':
action_t_1_batch = Q_Network.evaluate_critic(state_t_1_batch)
q_t_1 = Q_Network.evaluate_target_critic(state_t_1_batch)
for i in range(0, len(train_batch)):
if done_batch[i]:
action_t_batch[i][action_batch[i]] = reward_batch[i]
else:
action_t_batch[i][action_batch[i]] = reward_batch[
i] + GAMMA * q_t_1[i][np.argmax(action_t_1_batch[i])]
elif Exp == 'softmax':
action_t_1_batch = Q_Network.evaluate_target_critic(state_t_1_batch)
q_t_1 = Exploration.softV(action_t_1_batch, s_scale)
for i in range(0, len(train_batch)):
if done_batch[i]:
action_t_batch[i][action_batch[i]] = reward_batch[i]
else:
action_t_batch[i][
action_batch[i]] = reward_batch[i] + GAMMA * q_t_1[i]
elif Exp == 'sparsemax':
action_t_1_batch = Q_Network.evaluate_target_critic(state_t_1_batch)
q_t_1 = Exploration.sparsemax(action_t_1_batch, s_scale)
for i in range(0, len(train_batch)):
if done_batch[i]:
action_t_batch[i][action_batch[i]] = reward_batch[i]
else:
action_t_batch[i][
action_batch[i]] = reward_batch[i] + GAMMA * q_t_1[i]
# Update critic by minimizing the loss
Q_Network.train_critic(state_t_batch, action_t_batch)
def train_prioritized(Q_Network, train_batch, w_batch, Exp, s_scale, input_size, num_actions, size_action_batch):
state_t_batch, state_t_1_batch, action_batch, reward_batch, done_batch = zip(*train_batch)
state_t_batch = np.array(state_t_batch)
state_t_1_batch = np.array(state_t_1_batch)
action_batch = np.array(action_batch)
reward_batch = np.array(reward_batch)
done_batch = np.array(done_batch)
batch_size = len(done_batch)
q_t_1_batch = []
if Exp == 'epsilon':
# 'target-Q네트워크'로 계산한 action별 q값 계산
q_t_1_batch = Q_Network.get_target_q_batch(state_t_1_batch)
q_t_1_batch = np.reshape(q_t_1_batch, [batch_size, -1])
# Exploration 기법 별로 V(next_state)를 계산
q_t_1_batch = np.max(q_t_1_batch, axis = 1)
# 측정 Q(state, action)을 q_t_1_batch로 저장
q_t_1_batch = reward_batch + GAMMA*q_t_1_batch#*(1-done_batch)
elif Exp == 'softmax':
q_t_1_batch = Q_Network.get_target_q_batch(state_t_1_batch)
q_t_1_batch = np.reshape(q_t_1_batch, [batch_size, -1])
q_t_1_batch = Exploration.softV(q_t_1_batch, s_scale)
q_t_1_batch = reward_batch + GAMMA*q_t_1_batch#*(1-done_batch)
elif Exp == 'sparsemax':
q_t_1_batch = Q_Network.get_target_q_batch(state_t_1_batch)
q_t_1_batch = np.reshape(q_t_1_batch, [batch_size, -1])
q_t_1_batch = Exploration.sparsemax(q_t_1_batch, s_scale)
q_t_1_batch = reward_batch + GAMMA*q_t_1_batch#*(1-done_batch)
q_t_1_batch = np.reshape(q_t_1_batch,[-1,1])
w_batch = np.reshape(w_batch,[-1,1])
# q_t_1_batch와 오차가 가장 작아지는 방향으로 Q네트워크 training, weight 적용
errors, cost, _ = Q_Network.train_critic_prioritized(state_t_batch, action_batch, q_t_1_batch, w_batch)
errors = np.sum(errors, axis=1)
return errors, cost, state_t_batch
def train(Q_Network, train_batch, Exp, s_scale, input_size, num_actions, size_action_batch):
state_t_batch, state_t_1_batch, action_batch, reward_batch, done_batch = zip(*train_batch)
state_t_batch = np.array(state_t_batch)
state_t_1_batch = np.array(state_t_1_batch)
action_batch = np.array(action_batch)
reward_batch = np.array(reward_batch)
done_batch = np.array(done_batch)
q_t_1_batch = []
if Exp == 'epsilon':
for i in range(0, len(train_batch)):
q_t_1_batch.append(np.reshape(Q_Network.get_target_q_batch(np.reshape(state_t_1_batch[i],[1,-1])),[1,-1])[0])
q_t_1_batch = reward_batch + GAMMA*np.max(q_t_1_batch, axis=1)#*(1-done_batch)
elif Exp == 'softmax':
action_t_1_batch = Q_Network.evaluate_target_critic(state_t_1_batch)
q_t_1 = Exploration.softV(action_t_1_batch, s_scale)
for i in range(0, len(train_batch)):
if done_batch[i]:
action_t_batch[i][action_batch[i]] = reward_batch[i]
else:
action_t_batch[i][action_batch[i]] = reward_batch[i] + GAMMA*q_t_1[i]
elif Exp == 'sparsemax':
action_t_1_batch = Q_Network.evaluate_target_critic(state_t_1_batch)
q_t_1 = Exploration.sparsemax(action_t_1_batch, s_scale)
for i in range(0, len(train_batch)):
if done_batch[i]:
action_t_batch[i][action_batch[i]] = reward_batch[i]
else:
action_t_batch[i][action_batch[i]] = reward_batch[i] + GAMMA*q_t_1[i]
q_t_1_batch = np.reshape(q_t_1_batch,[-1,1])
cost, _ = Q_Network.train_critic(state_t_batch, action_batch, q_t_1_batch)
return cost, state_t_batch