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Refactoring demo collector (#56)

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This commit is contained in:
Donghyeon Kim
2019-08-17 14:29:58 +09:00
committed by Whi Kwon
parent 055d97661e
commit 18d6824191
19 changed files with 972 additions and 20 deletions
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scripts/algorithms/common/abstract/agent.py
+2 -2
View File
@@ -80,7 +80,7 @@ class AbstractAgent(object):
path = os.path.join("./save/" + save_name + "_ep_" + str(n_episode) + ".pt")
torch.save(params, path)
print ("[INFO] Saved the model and optimizer to", path)
print("[INFO] Saved the model and optimizer to", path)
@abstractmethod
def write_log(self, *args):
@@ -109,7 +109,7 @@ class AbstractAgent(object):
score += reward
step += 1
print (
print(
"[INFO] episode %d\tstep: %d\ttotal score: %d"
% (i_episode, step, score)
)
scripts/algorithms/fd/sac_agent.py
+1 -1
View File
@@ -201,7 +201,7 @@ class Agent(SACAgent):
def pretrain(self):
"""Pretraining steps."""
pretrain_loss = list()
print ("[INFO] Pre-Train %d steps." % self.hyper_params["PRETRAIN_STEP"])
print("[INFO] Pre-Train %d steps." % self.hyper_params["PRETRAIN_STEP"])
for i_step in range(1, self.hyper_params["PRETRAIN_STEP"] + 1):
loss = self.update_model()
pretrain_loss.append(loss) # for logging
scripts/algorithms/fd/td3_agent.py
+1 -1
View File
@@ -180,7 +180,7 @@ class Agent(TD3Agent):
def pretrain(self):
"""Pretraining steps."""
pretrain_loss = list()
print ("[INFO] Pre-Train %d steps." % self.hyper_params["PRETRAIN_STEP"])
print("[INFO] Pre-Train %d steps." % self.hyper_params["PRETRAIN_STEP"])
for i_step in range(1, self.hyper_params["PRETRAIN_STEP"] + 1):
loss = self.update_model()
pretrain_loss.append(loss) # for logging
scripts/algorithms/sac/agent.py
+3 -3
View File
@@ -264,7 +264,7 @@ class Agent(AbstractAgent):
def load_params(self, path):
"""Load model and optimizer parameters."""
if not os.path.exists(path):
print ("[ERROR] the input path does not exist. ->", path)
print("[ERROR] the input path does not exist. ->", path)
return
params = torch.load(path)
@@ -281,7 +281,7 @@ class Agent(AbstractAgent):
if self.hyper_params["AUTO_ENTROPY_TUNING"]:
self.alpha_optimizer.load_state_dict(params["alpha_optim"])
print ("[INFO] loaded the model and optimizer from", path)
print("[INFO] loaded the model and optimizer from", path)
def save_params(self, n_episode):
"""Save model and optimizer parameters."""
@@ -306,7 +306,7 @@ class Agent(AbstractAgent):
"""Write log about loss and score"""
total_loss = loss.sum()
print (
print(
"[INFO] episode %d, episode_step %d, total step %d, total score: %d\n"
"total loss: %.3f actor_loss: %.3f qf_1_loss: %.3f qf_2_loss: %.3f "
"vf_loss: %.3f alpha_loss: %.3f\n"
scripts/algorithms/td3/agent.py
+3 -3
View File
@@ -177,7 +177,7 @@ class Agent(AbstractAgent):
def load_params(self, path):
"""Load model and optimizer parameters."""
if not os.path.exists(path):
print ("[ERROR] the input path does not exist. ->", path)
print("[ERROR] the input path does not exist. ->", path)
return
params = torch.load(path)
@@ -189,7 +189,7 @@ class Agent(AbstractAgent):
self.critic2_target.load_state_dict(params["critic2_target_state_dict"])
self.actor_optim.load_state_dict(params["actor_optim_state_dict"])
self.critic_optim.load_state_dict(params["critic_optim_state_dict"])
print ("[INFO] loaded the model and optimizer from", path)
print("[INFO] loaded the model and optimizer from", path)
def save_params(self, n_episode):
"""Save model and optimizer parameters."""
@@ -210,7 +210,7 @@ class Agent(AbstractAgent):
"""Write log about loss and score"""
total_loss = loss.sum()
print (
print(
"[INFO] total_steps: %d episode: %d total score: %d, total loss: %f\n"
"actor_loss: %.3f critic1_loss: %.3f critic2_loss: %.3f\n"
% (self.total_steps, i, score, total_loss, loss[0], loss[1], loss[2])