diff --git a/convlab/policy/ppo/README.md b/convlab/policy/ppo/README.md
index c762253ce4fe769bb2c540b39d39df713881a7f3..9efb621bcaa49cdcaba0446b90c5c80f77f77876 100755
--- a/convlab/policy/ppo/README.md
+++ b/convlab/policy/ppo/README.md
@@ -21,7 +21,7 @@ One example for the environment-config is **semantic_level_config.json**, where
- num_eval_dialogues: how many evaluation dialogues should be used
- epoch: how many training epochs to run. One epoch consists of collecting dialogues + performing an update
- eval_frequency: after how many epochs perform an evaluation
-- batchsz: the number of training dialogues collected before doing an update
+- num_train_dialogues: the number of training dialogues collected before doing an update
Moreover, you can specify the full dialogue pipeline here, such as the user policy, NLU for system and user, etc.
diff --git a/convlab/policy/ppo/semanticGenTUS-RuleDST-PPOPolicy.json b/convlab/policy/ppo/semanticGenTUS-RuleDST-PPOPolicy.json
index 0e8774e20898c2855f368127f8e14e193ac2c21d..e0a4a4ec50d0ed81347e14dc4f426049082b5036 100644
--- a/convlab/policy/ppo/semanticGenTUS-RuleDST-PPOPolicy.json
+++ b/convlab/policy/ppo/semanticGenTUS-RuleDST-PPOPolicy.json
@@ -3,9 +3,9 @@
"load_path": "convlab/policy/ppo/pretrained_models/mle",
"pretrained_load_path": "",
"use_pretrained_initialisation": false,
- "batchsz": 500,
+ "num_train_dialogues": 100,
"seed": 0,
- "epoch": 50,
+ "epoch": 500,
"eval_frequency": 5,
"process_num": 1,
"num_eval_dialogues": 500,
diff --git a/convlab/policy/ppo/semantic_level_config.json b/convlab/policy/ppo/semantic_level_config.json
index 04b0626a10bc8d48add16732df26a7cc00a35088..fd627b1579c4fccfbcb3ea95b3f0caf8348e7b96 100644
--- a/convlab/policy/ppo/semantic_level_config.json
+++ b/convlab/policy/ppo/semantic_level_config.json
@@ -3,9 +3,9 @@
"load_path": "",
"use_pretrained_initialisation": false,
"pretrained_load_path": "",
- "batchsz": 1000,
+ "num_train_dialogues": 100,
"seed": 0,
- "epoch": 10,
+ "epoch": 100,
"eval_frequency": 5,
"process_num": 4,
"sys_semantic_to_usr": false,
diff --git a/convlab/policy/ppo/setsumbt_config.json b/convlab/policy/ppo/setsumbt_config.json
index b6a02adbf371bfea63e3e156a2d9e47f13456c78..f3e7e4423989643bac4c1f279b2f4738602e6ffd 100644
--- a/convlab/policy/ppo/setsumbt_config.json
+++ b/convlab/policy/ppo/setsumbt_config.json
@@ -3,9 +3,9 @@
"load_path": "",
"pretrained_load_path": "",
"use_pretrained_initialisation": false,
- "batchsz": 1000,
+ "num_train_dialogues": 100,
"seed": 0,
- "epoch": 50,
+ "epoch": 500,
"eval_frequency": 5,
"process_num": 2,
"num_eval_dialogues": 500,
diff --git a/convlab/policy/ppo/setsumbt_unc_config.json b/convlab/policy/ppo/setsumbt_unc_config.json
index fafdb3fc9bd8f7fe09e3759d58a591cf964fb93b..df95e0fbd9a8acac03668ec8092a7d0272c91413 100644
--- a/convlab/policy/ppo/setsumbt_unc_config.json
+++ b/convlab/policy/ppo/setsumbt_unc_config.json
@@ -3,9 +3,9 @@
"load_path": "",
"pretrained_load_path": "",
"use_pretrained_initialisation": false,
- "batchsz": 1000,
+ "num_train_dialogues": 100,
"seed": 0,
- "epoch": 50,
+ "epoch": 500,
"eval_frequency": 5,
"process_num": 2,
"num_eval_dialogues": 500,
diff --git a/convlab/policy/ppo/train.py b/convlab/policy/ppo/train.py
index 703a55005b8c07578b85765a626d9871deebf26e..42fd9425d1416555da52073f15635cf7cfd99997 100755
--- a/convlab/policy/ppo/train.py
+++ b/convlab/policy/ppo/train.py
@@ -33,7 +33,7 @@ except RuntimeError:
pass
-def sampler(pid, queue, evt, env, policy, batchsz, train_seed=0):
+def sampler(pid, queue, evt, env, policy, num_dialogues, train_seed=0):
"""
This is a sampler function, and it will be called by multiprocess.Process to sample data from environment by multiple
@@ -60,7 +60,7 @@ def sampler(pid, queue, evt, env, policy, batchsz, train_seed=0):
set_seed(train_seed)
- while sampled_num < batchsz:
+ while sampled_traj_num < num_dialogues:
# for each trajectory, we reset the env and get initial state
s = env.reset()
for t in range(traj_len):
@@ -108,7 +108,7 @@ def sampler(pid, queue, evt, env, policy, batchsz, train_seed=0):
evt.wait()
-def sample(env, policy, batchsz, process_num, seed):
+def sample(env, policy, num_train_dialogues, process_num, seed):
"""
Given batchsz number of task, the batchsz will be splited equally to each processes
@@ -122,7 +122,7 @@ def sample(env, policy, batchsz, process_num, seed):
# batchsz will be splitted into each process,
# final batchsz maybe larger than batchsz parameters
- process_batchsz = np.ceil(batchsz / process_num).astype(np.int32)
+ process_num_dialogues = np.ceil(num_train_dialogues / process_num).astype(np.int32)
train_seeds = random.sample(range(0, 1000), process_num)
# buffer to save all data
queue = mp.Queue()
@@ -137,7 +137,7 @@ def sample(env, policy, batchsz, process_num, seed):
evt = mp.Event()
processes = []
for i in range(process_num):
- process_args = (i, queue, evt, env, policy, process_batchsz, train_seeds[i])
+ process_args = (i, queue, evt, env, policy, process_num_dialogues, train_seeds[i])
processes.append(mp.Process(target=sampler, args=process_args))
for p in processes:
# set the process as daemon, and it will be killed once the main process is stoped.
@@ -157,10 +157,10 @@ def sample(env, policy, batchsz, process_num, seed):
return buff.get_batch()
-def update(env, policy, batchsz, epoch, process_num, seed=0):
+def update(env, policy, num_dialogues, epoch, process_num, seed=0):
# sample data asynchronously
- batch = sample(env, policy, batchsz, process_num, seed)
+ batch = sample(env, policy, num_dialogues, process_num, seed)
# print(batch)
# data in batch is : batch.state: ([1, s_dim], [1, s_dim]...)
@@ -224,7 +224,7 @@ if __name__ == '__main__':
logging.info("Policy initialised from scratch")
log_start_args(conf)
- logging.info(f"New episodes per epoch: {conf['model']['batchsz']}")
+ logging.info(f"New episodes per epoch: {conf['model']['num_train_dialogues']}")
env, sess = env_config(conf, policy_sys)
@@ -250,11 +250,11 @@ if __name__ == '__main__':
for i in range(conf['model']['epoch']):
idx = i + 1
# print("Epoch :{}".format(str(idx)))
- update(env, policy_sys, conf['model']['batchsz'], idx, conf['model']['process_num'], seed=seed)
+ update(env, policy_sys, conf['model']['num_train_dialogues'], idx, conf['model']['process_num'], seed=seed)
if idx % conf['model']['eval_frequency'] == 0 and idx != 0:
time_now = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
- logging.info(f"Evaluating after Dialogues: {idx * conf['model']['batchsz']} - {time_now}" + '-' * 60)
+ logging.info(f"Evaluating after Dialogues: {idx * conf['model']['num_train_dialogues']} - {time_now}" + '-' * 60)
eval_dict = eval_policy(conf, policy_sys, env, sess, save_eval, log_save_path)
@@ -264,7 +264,7 @@ if __name__ == '__main__':
eval_dict["avg_return"], save_path)
policy_sys.save(save_path, "last")
for key in eval_dict:
- tb_writer.add_scalar(key, eval_dict[key], idx * conf['model']['batchsz'])
+ tb_writer.add_scalar(key, eval_dict[key], idx * conf['model']['num_train_dialogues'])
logging.info("End of Training: " +
time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()))
diff --git a/convlab/policy/ppo/trippy_config.json b/convlab/policy/ppo/trippy_config.json
index 41b1c3623aca944312c6389e55e34d72422fb6e0..31430bfc41a1004078b64ff3bef755dc0a9f65b3 100644
--- a/convlab/policy/ppo/trippy_config.json
+++ b/convlab/policy/ppo/trippy_config.json
@@ -3,9 +3,9 @@
"load_path": "/path/to/model/checkpoint",
"pretrained_load_path": "",
"use_pretrained_initialisation": false,
- "batchsz": 1000,
+ "num_train_dialogues": 100,
"seed": 0,
- "epoch": 50,
+ "epoch": 500,
"eval_frequency": 5,
"process_num": 2,
"num_eval_dialogues": 500,
diff --git a/convlab/policy/ppo/tus_semantic_level_config.json b/convlab/policy/ppo/tus_semantic_level_config.json
index 9d56646cc857de85b47a1f9925a0e4bf89d8b524..14dacf9e5b45fc0264b07624a4ccb1fe0d4d4352 100644
--- a/convlab/policy/ppo/tus_semantic_level_config.json
+++ b/convlab/policy/ppo/tus_semantic_level_config.json
@@ -3,9 +3,9 @@
"load_path": "convlab/policy/ppo/pretrained_models/mle",
"use_pretrained_initialisation": false,
"pretrained_load_path": "",
- "batchsz": 1000,
+ "num_train_dialogues": 100,
"seed": 0,
- "epoch": 50,
+ "epoch": 500,
"eval_frequency": 5,
"process_num": 4,
"sys_semantic_to_usr": false,