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fix cql bugs #2

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69 changes: 54 additions & 15 deletions strategy_train_env/bidding_train_env/baseline/cql/cql.py
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Original file line number Diff line number Diff line change
Expand Up @@ -85,6 +85,7 @@ def __init__(self, dim_obs=2, dim_actions=1, gamma=1, tau=0.001, V_lr=1e-4, crit
self.log_alpha = torch.zeros(1, requires_grad=True)
self.alpha_optimizer = optim.Adam([self.log_alpha], lr=actor_lr)
self.min_q_weight = 1.0
self.num_random = 10

self.to(self.device)

Expand All @@ -97,7 +98,7 @@ def build_network(self, input_dim: int, output_dim: int) -> nn.Sequential:
nn.Linear(256, output_dim),
)

def step(self, states: torch.Tensor, actions: torch.Tensor, rewards: torch.Tensor, next_states: torch.Tensor, dones: torch.Tensor):
def step(self, states: torch.Tensor, actions: torch.Tensor, rewards: torch.Tensor, next_states: torch.Tensor, dones: torch.Tensor, step_num: int = 0) -> tuple:
'''
train model
'''
Expand All @@ -124,6 +125,15 @@ def step(self, states: torch.Tensor, actions: torch.Tensor, rewards: torch.Tenso
)

policy_loss = (alpha * log_pi - q_new_actions).mean()
if step_num < 2000:
"""
For the initial few epochs, try doing behaivoral cloning, if needed
conventionally, there's not much difference in performance with having 20k
gradient steps here, or not having it
"""
policy_log_prob = self.policy.compute_log_prob(actions, states)
policy_loss = (alpha * log_pi - policy_log_prob).mean()

self.policy_optimizer.zero_grad()
policy_loss.backward()
self.policy_optimizer.step()
Expand All @@ -140,16 +150,24 @@ def step(self, states: torch.Tensor, actions: torch.Tensor, rewards: torch.Tenso
self.target_qf2(torch.cat([next_states, next_actions], dim=-1))
)
target_q_values = rewards + (1. - dones) * self.gamma * (target_q_values - alpha * next_log_pi)
target_q_values = target_q_values.detach()
#print("target_q_values",target_q_values)

# CQL Regularization
# Ensure the generated action range is broader than data action range for effective training
random_actions = torch.FloatTensor(states.shape[0], actions.shape[-1]).uniform_(-1000, 1000).to(self.device)
q1_rand = self.qf1(torch.cat([states, random_actions], dim=-1))
q2_rand = self.qf2(torch.cat([states, random_actions], dim=-1))
random_actions = torch.FloatTensor(states.shape[0] * self.num_random, actions.shape[-1]).uniform_(0, 300).to(self.device)
curr_actions_tensor = self._get_policy_actions(states, self.num_random, self.policy)
next_actions_tensor = self._get_policy_actions(next_states, self.num_random, self.policy)
q1_rand = self._get_tensor_values(states, random_actions, self.qf1)
q2_rand = self._get_tensor_values(states, random_actions, self.qf2)
q1_curr_actions = self._get_tensor_values(states, curr_actions_tensor, self.qf1)
q2_curr_actions = self._get_tensor_values(states, curr_actions_tensor, self.qf2)
q1_next_actions = self._get_tensor_values(next_states, next_actions_tensor, self.qf1)
q2_next_actions = self._get_tensor_values(next_states, next_actions_tensor, self.qf2)


cat_q1 = torch.cat([q1_rand, q1_pred, q1_pred], 1)
cat_q2 = torch.cat([q2_rand, q2_pred, q2_pred], 1)
cat_q1 = torch.cat([q1_rand, q1_pred.unsqueeze(1), q1_curr_actions, q1_next_actions], 1)
cat_q2 = torch.cat([q2_rand, q2_pred.unsqueeze(1), q2_curr_actions, q2_next_actions], 1)

min_qf1_loss = torch.logsumexp(cat_q1 / self.temperature,
dim=1).mean() * self.min_q_weight * self.temperature - q1_pred.mean() * self.min_q_weight
Expand All @@ -160,15 +178,18 @@ def step(self, states: torch.Tensor, actions: torch.Tensor, rewards: torch.Tenso
diff1 = target_q_values - q1_pred
diff2 = target_q_values - q2_pred

qf1_loss = torch.where(diff1 > 0, self.expectile * diff1 ** 2, (1 - self.expectile) * diff1 ** 2).mean() + min_qf1_loss
qf2_loss = torch.where(diff2 > 0, self.expectile * diff2 ** 2, (1 - self.expectile) * diff2 ** 2).mean() + min_qf2_loss
qf1_loss = torch.nn.MSELoss()(q1_pred, target_q_values) + min_qf1_loss
qf2_loss = torch.nn.MSELoss()(q2_pred, target_q_values) + min_qf2_loss

#qf1_loss = torch.where(diff1 > 0, self.expectile * diff1 ** 2, (1 - self.expectile) * diff1 ** 2).mean() + min_qf1_loss
#qf2_loss = torch.where(diff2 > 0, self.expectile * diff2 ** 2, (1 - self.expectile) * diff2 ** 2).mean() + min_qf2_loss

self.qf1_optimizer.zero_grad()
qf1_loss.backward()
qf1_loss.backward(retain_graph=True)
self.qf1_optimizer.step()

self.qf2_optimizer.zero_grad()
qf2_loss.backward()
qf2_loss.backward(retain_graph=True)
self.qf2_optimizer.step()

# Soft update target networks
Expand Down Expand Up @@ -209,14 +230,14 @@ def save_net(self, save_path: str) -> None:
torch.save(self.qf2.state_dict(), save_path + "/qf2" + ".pkl")
torch.save(self.policy.state_dict(), save_path + "/policy" + ".pkl")

def save_jit(self, save_path: str) -> None:
def save_jit(self, save_path: str, seed:int, step:int) -> None:
'''
save model as JIT
'''
if not os.path.isdir(save_path):
os.makedirs(save_path)
scripted_policy = torch.jit.script(self.cpu())
scripted_policy.save(save_path + "/cql_model" + ".pth")
scripted_policy.save(save_path + "/cql_model" + "_" +str(seed) + "_" +str(step) + ".pth")

def load_net(self, load_path="saved_model/fixed_initial_budget", device='cuda:0') -> None:
'''
Expand All @@ -232,6 +253,24 @@ def load_net(self, load_path="saved_model/fixed_initial_budget", device='cuda:0'
self.target_qf1.to(self.device)
self.target_qf2.to(self.device)

def _get_tensor_values(self, obs, actions, network=None):
action_shape = actions.shape[0]
obs_shape = obs.shape[0]
num_repeat = int (action_shape / obs_shape)
obs_temp = obs.unsqueeze(1).repeat(1, num_repeat, 1).view(obs.shape[0] * num_repeat, obs.shape[1])
state_action_input = torch.cat([obs_temp, actions], dim=-1)
preds = network(state_action_input)
preds = preds.view(obs.shape[0], num_repeat, 1)
return preds

def _get_policy_actions(self, obs, num_actions, network=None):
obs_temp = obs.unsqueeze(1).repeat(1, num_actions, 1).view(obs.shape[0] * num_actions, obs.shape[1])
new_obs_actions = network(
obs_temp, reparameterize=True
)
return new_obs_actions


if __name__ == '__main__':
model = CQL(dim_obs=2)
model.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
Expand All @@ -254,10 +293,10 @@ def load_net(self, load_path="saved_model/fixed_initial_budget", device='cuda:0'
dtype=torch.float), torch.tensor(
terminals, dtype=torch.float)

q_loss, v_loss, a_loss = model.step(states, actions, rewards, next_states, terminals)
print(f'step:{i} q_loss:{q_loss} v_loss:{v_loss} a_loss:{a_loss}')
q1_loss, q2_loss, a_loss = model.step(states, actions, rewards, next_states, terminals)

#print(f'step:{i} q_loss:{q_loss} v_loss:{v_loss} a_loss:{a_loss}')

total_params = sum(p.numel() for p in model.parameters())
print("Learnable parameters: {:,}".format(total_params))


7 changes: 4 additions & 3 deletions strategy_train_env/main/main_cql.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,8 +6,9 @@

from run.run_cql import run_cql

torch.manual_seed(1)
np.random.seed(1)
seed = 3
torch.manual_seed(seed)
np.random.seed(seed)

if __name__ == "__main__":
run_cql()
run_cql(seed=seed)
30 changes: 18 additions & 12 deletions strategy_train_env/run/run_cql.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,14 +8,15 @@
import pandas as pd
import ast
import pickle
from torch.utils.tensorboard import SummaryWriter
# Configure logging
logging.basicConfig(level=logging.INFO,
format="[%(asctime)s] [%(name)s] [%(filename)s(%(lineno)d)] [%(levelname)s] %(message)s")
logger = logging.getLogger(__name__)

STATE_DIM = 16

def train_cql_model():
def train_cql_model(seed=1):
"""
Train the CQL model.
"""
Expand Down Expand Up @@ -45,15 +46,14 @@ def safe_literal_eval(val):
# Build replay buffer
replay_buffer = ReplayBuffer()
add_to_replay_buffer(replay_buffer, training_data, is_normalize)
print(len(replay_buffer.memory))

# Train model
model = CQL(dim_obs=STATE_DIM)
train_model_steps(model, replay_buffer)
train_model_steps(model, replay_buffer, seed=seed)

# Save model
# model.save_net("saved_model/CQLtest")
model.save_jit("saved_model/CQLtest")
#model.save_jit("saved_model/CQLtest", seed=seed, step=i)

# Test trained model
test_trained_model(model, replay_buffer)
Expand All @@ -69,13 +69,20 @@ def add_to_replay_buffer(replay_buffer, training_data, is_normalize):
replay_buffer.push(np.array(state), np.array([action]), np.array([reward]), np.zeros_like(state),
np.array([done]))

def train_model_steps(model, replay_buffer, step_num=100, batch_size=100):
def train_model_steps(model, replay_buffer, step_num=100000, batch_size=100, seed=1):
for i in range(step_num):
if i==8000:
pass
states, actions, rewards, next_states, terminals = replay_buffer.sample(batch_size)
q_loss, v_loss, a_loss = model.step(states, actions, rewards, next_states, terminals)
logger.info(f'Step: {i} Q_loss: {q_loss} V_loss: {v_loss} A_loss: {a_loss}')
q1_loss, q2_loss, policy_loss = model.step(states, actions, rewards, next_states, terminals, i)
if i == 0:
writer = SummaryWriter(log_dir="tensorboard/CQL/" + str(seed))
writer.add_scalar('Loss/q1_loss', q1_loss, i)
writer.add_scalar('Loss/q2_loss', q2_loss, i)
writer.add_scalar('Loss/policy_loss', policy_loss, i)
if i == step_num - 1:
writer.close()
if i+1 % 10000 == 0:
model.save_jit("saved_model/CQLtest", seed=seed, step=i+1)
#logger.info(f'Step: {i} Q_loss: {q_loss} V_loss: {v_loss} A_loss: {a_loss}')

def test_trained_model(model, replay_buffer):
for i in range(100):
Expand All @@ -85,12 +92,11 @@ def test_trained_model(model, replay_buffer):
tem = np.concatenate((actions, pred_actions), axis=1)
print("concate:",tem)

def run_cql():
print(sys.path)
def run_cql(seed=1):
"""
Run CQL model training and evaluation.
"""
train_cql_model()
train_cql_model(seed=seed)

if __name__ == '__main__':
run_cql()