def _init_model(self):
"""init model from parameters"""
self.env, env_continuous, num_states, self.num_actions = get_env_info(self.env_id)
assert env_continuous, "SAC is only applicable to continuous environment !!!!"
self.action_low, self.action_high = self.env.action_space.low[0], self.env.action_space.high[0]
# seeding
np.random.seed(self.seed)
torch.manual_seed(self.seed)
self.env.seed(self.seed)
self.policy_net = Policy(num_states, self.num_actions, max_action=self.action_high).double().to(device)
self.value_net = Value(num_states).double().to(device)
self.value_net_target = Value(num_states).double().to(device)
self.q_net_1 = QValue(num_states, self.num_actions).double().to(device)
self.q_net_2 = QValue(num_states, self.num_actions).double().to(device)
self.running_state = ZFilter((num_states,), clip=5)
if self.model_path:
print("Loading Saved Model {}_sac.p".format(self.env_id))
self.policy_net, self.value_net, self.q_net_1, self.q_net_2, self.running_state \
= pickle.load(open('{}/{}_sac.p'.format(self.model_path, self.env_id), "rb"))
self.value_net_target.load_state_dict(self.value_net.state_dict())
self.optimizer_p = optim.Adam(self.policy_net.parameters(), lr=self.lr_p)
self.optimizer_v = optim.Adam(self.value_net.parameters(), lr=self.lr_v)
self.optimizer_q_1 = optim.Adam(self.q_net_1.parameters(), lr=self.lr_q)
self.optimizer_q_2 = optim.Adam(self.q_net_2.parameters(), lr=self.lr_q)
def _init_model(self):
"""init model from parameters"""
self.env, env_continuous, num_states, num_actions = get_env_info(
self.env_id)
# seeding
torch.manual_seed(self.seed)
self.env.seed(self.seed)
if env_continuous:
self.policy_net = Policy(num_states, num_actions).to(device)
else:
self.policy_net = DiscretePolicy(num_states,
num_actions).to(device)
self.value_net = Value(num_states).to(device)
self.ac_net = Actor_Critic(self.policy_net, self.value_net).to(device)
self.running_state = ZFilter((num_states, ), clip=5)
if self.model_path:
print("Loading Saved Model {}_a2c.p".format(self.env_id))
self.ac_net, self.running_state = pickle.load(
open('{}/{}_a2c.p'.format(self.model_path, self.env_id), "rb"))
self.collector = MemoryCollector(self.env,
self.ac_net,
render=self.render,
running_state=self.running_state,
num_process=self.num_process)
self.optimizer_ac = optim.Adam(self.ac_net.parameters(), lr=self.lr_ac)
def _init_model(self):
"""init model from parameters"""
self.env, env_continuous, num_states, self.num_actions = get_env_info(
self.env_id)
assert env_continuous, "SAC is only applicable to continuous environment !!!!"
self.action_low, self.action_high = self.env.action_space.low[
0], self.env.action_space.high[0]
self.target_entropy = -np.prod(self.env.action_space.shape)
# seeding
np.random.seed(self.seed)
torch.manual_seed(self.seed)
self.env.seed(self.seed)
self.policy_net = Policy(num_states,
self.num_actions,
max_action=self.action_high,
use_sac=True).double().to(device)
self.q_net_1 = QValue(num_states, self.num_actions).double().to(device)
self.q_net_target_1 = QValue(num_states,
self.num_actions).double().to(device)
self.q_net_2 = QValue(num_states, self.num_actions).double().to(device)
self.q_net_target_2 = QValue(num_states,
self.num_actions).double().to(device)
# self.alpha init
self.alpha = torch.exp(torch.zeros(
1, device=device).double()).requires_grad_()
self.running_state = ZFilter((num_states, ), clip=5)
if self.model_path:
print("Loading Saved Model {}_sac_alpha.p".format(self.env_id))
self.policy_net, self.q_net_1, self.q_net_2, self.running_state \
= pickle.load(open('{}/{}_sac_alpha.p'.format(self.model_path, self.env_id), "rb"))
self.q_net_target_1.load_state_dict(self.q_net_1.state_dict())
self.q_net_target_2.load_state_dict(self.q_net_2.state_dict())
self.optimizer_p = optim.Adam(self.policy_net.parameters(),
lr=self.lr_p)
self.optimizer_a = optim.Adam([self.alpha], lr=self.lr_a)
self.optimizer_q_1 = optim.Adam(self.q_net_1.parameters(),
lr=self.lr_q)
self.optimizer_q_2 = optim.Adam(self.q_net_2.parameters(),
lr=self.lr_q)
class SAC_Alpha:
def __init__(self,
env_id,
render=False,
num_process=1,
memory_size=1000000,
lr_p=1e-3,
lr_a=3e-4,
lr_q=1e-3,
gamma=0.99,
polyak=0.995,
explore_size=10000,
step_per_iter=3000,
batch_size=100,
min_update_step=1000,
update_step=50,
target_update_delay=1,
seed=1,
model_path=None):
self.env_id = env_id
self.gamma = gamma
self.polyak = polyak
self.memory = Memory(memory_size)
self.explore_size = explore_size
self.step_per_iter = step_per_iter
self.render = render
self.num_process = num_process
self.lr_p = lr_p
self.lr_a = lr_a
self.lr_q = lr_q
self.batch_size = batch_size
self.min_update_step = min_update_step
self.update_step = update_step
self.target_update_delay = target_update_delay
self.model_path = model_path
self.seed = seed
self._init_model()
def _init_model(self):
"""init model from parameters"""
self.env, env_continuous, num_states, self.num_actions = get_env_info(
self.env_id)
assert env_continuous, "SAC is only applicable to continuous environment !!!!"
self.action_low, self.action_high = self.env.action_space.low[
0], self.env.action_space.high[0]
self.target_entropy = -np.prod(self.env.action_space.shape)
# seeding
np.random.seed(self.seed)
torch.manual_seed(self.seed)
self.env.seed(self.seed)
self.policy_net = Policy(num_states,
self.num_actions,
max_action=self.action_high,
use_sac=True).to(device)
self.q_net_1 = QValue(num_states, self.num_actions).to(device)
self.q_net_target_1 = QValue(num_states, self.num_actions).to(device)
self.q_net_2 = QValue(num_states, self.num_actions).to(device)
self.q_net_target_2 = QValue(num_states, self.num_actions).to(device)
# self.alpha init
self.alpha = torch.exp(torch.zeros(1, device=device)).requires_grad_()
self.running_state = ZFilter((num_states, ), clip=5)
if self.model_path:
print("Loading Saved Model {}_sac_alpha.p".format(self.env_id))
self.policy_net, self.q_net_1, self.q_net_2, self.running_state \
= pickle.load(open('{}/{}_sac_alpha.p'.format(self.model_path, self.env_id), "rb"))
self.q_net_target_1.load_state_dict(self.q_net_1.state_dict())
self.q_net_target_2.load_state_dict(self.q_net_2.state_dict())
self.optimizer_p = optim.Adam(self.policy_net.parameters(),
lr=self.lr_p)
self.optimizer_a = optim.Adam([self.alpha], lr=self.lr_a)
self.optimizer_q_1 = optim.Adam(self.q_net_1.parameters(),
lr=self.lr_q)
self.optimizer_q_2 = optim.Adam(self.q_net_2.parameters(),
lr=self.lr_q)
def choose_action(self, state):
"""select action"""
state = FLOAT(state).unsqueeze(0).to(device)
with torch.no_grad():
action, _ = self.policy_net.rsample(state)
action = action.cpu().numpy()[0]
return action, None
def eval(self, i_iter, render=False):
"""evaluate model"""
state = self.env.reset()
test_reward = 0
while True:
if render:
self.env.render()
state = self.running_state(state)
action, _ = self.choose_action(state)
state, reward, done, _ = self.env.step(action)
test_reward += reward
if done:
break
print(f"Iter: {i_iter}, test Reward: {test_reward}")
self.env.close()
def learn(self, writer, i_iter):
"""interact"""
global_steps = (i_iter - 1) * self.step_per_iter + 1
log = dict()
num_steps = 0
num_episodes = 0
total_reward = 0
min_episode_reward = float('inf')
max_episode_reward = float('-inf')
while num_steps < self.step_per_iter:
state = self.env.reset()
state = self.running_state(state)
episode_reward = 0
for t in range(10000):
if self.render:
self.env.render()
if global_steps < self.explore_size: # explore
action = self.env.action_space.sample()
else: # action
action, _ = self.choose_action(state)
next_state, reward, done, _ = self.env.step(action)
next_state = self.running_state(next_state)
mask = 0 if done else 1
# ('state', 'action', 'reward', 'next_state', 'mask', 'log_prob')
self.memory.push(state, action, reward, next_state, mask, None)
episode_reward += reward
global_steps += 1
num_steps += 1
if global_steps >= self.min_update_step and global_steps % self.update_step == 0:
for k in range(1, self.update_step + 1):
batch = self.memory.sample(
self.batch_size) # random sample batch
self.update(batch, k)
if done or num_steps >= self.step_per_iter:
break
state = next_state
num_episodes += 1
total_reward += episode_reward
min_episode_reward = min(episode_reward, min_episode_reward)
max_episode_reward = max(episode_reward, max_episode_reward)
self.env.close()
log['num_steps'] = num_steps
log['num_episodes'] = num_episodes
log['total_reward'] = total_reward
log['avg_reward'] = total_reward / num_episodes
log['max_episode_reward'] = max_episode_reward
log['min_episode_reward'] = min_episode_reward
print(
f"Iter: {i_iter}, num steps: {log['num_steps']}, total reward: {log['total_reward']: .4f}, "
f"min reward: {log['min_episode_reward']: .4f}, max reward: {log['max_episode_reward']: .4f}, "
f"average reward: {log['avg_reward']: .4f}")
# record reward information
writer.add_scalar("total reward", log['total_reward'], i_iter)
writer.add_scalar("average reward", log['avg_reward'], i_iter)
writer.add_scalar("min reward", log['min_episode_reward'], i_iter)
writer.add_scalar("max reward", log['max_episode_reward'], i_iter)
writer.add_scalar("num steps", log['num_steps'], i_iter)
def update(self, batch, k_iter):
"""learn model"""
batch_state = FLOAT(batch.state).to(device)
batch_action = FLOAT(batch.action).to(device)
batch_reward = FLOAT(batch.reward).to(device)
batch_next_state = FLOAT(batch.next_state).to(device)
batch_mask = FLOAT(batch.mask).to(device)
# update by SAC Alpha
alg_step_stats = sac_alpha_step(
self.policy_net, self.q_net_1, self.q_net_2, self.alpha,
self.q_net_target_1, self.q_net_target_2, self.optimizer_p,
self.optimizer_q_1, self.optimizer_q_2, self.optimizer_a,
batch_state, batch_action, batch_reward, batch_next_state,
batch_mask, self.gamma, self.polyak, self.target_entropy,
k_iter % self.target_update_delay == 0)
def save(self, save_path):
"""save model"""
check_path(save_path)
pickle.dump(
(self.policy_net, self.q_net_1, self.q_net_2, self.running_state),
open('{}/{}_sac_alpha.p'.format(save_path, self.env_id), 'wb'))