polish(pu): polish resume_training in entry

ding/entry/serial_entry.py

@@ -54,7 +54,7 @@ def serial_pipeline(
         auto=True,
         create_cfg=create_cfg,
         save_cfg=True,
-        renew_dir=not cfg.policy.learn.resume_training
+        renew_dir=not cfg.policy.learn.get('resume_training', False)
     )
     # Create main components: env, policy
     if env_setting is None:
@@ -94,7 +94,7 @@ def serial_pipeline(
     # ==========
     # Learner's before_run hook.
     learner.call_hook('before_run')
-    if cfg.policy.learn.resume_training:
+    if cfg.policy.learn.get('resume_training', False):
         collector.envstep = learner.collector_envstep
 
     # Accumulate plenty of data at the beginning of training.

ding/entry/serial_entry_mbrl.py

@@ -37,7 +37,7 @@ def mbrl_entry_setup(
         auto=True,
         create_cfg=create_cfg,
         save_cfg=True,
-        renew_dir=not cfg.policy.learn.resume_training
+        renew_dir=not cfg.policy.learn.get('resume_training', False)
     )
 
     if env_setting is None:
@@ -79,8 +79,7 @@ def mbrl_entry_setup(
     )
 
     return (
-        cfg, policy, world_model, env_buffer, learner, collector, collector_env, evaluator, commander, tb_logger,
-        resume_training
+        cfg, policy, world_model, env_buffer, learner, collector, collector_env, evaluator, commander, tb_logger
     )
 
 
@@ -125,13 +124,13 @@ def serial_pipeline_dyna(
     Returns:
         - policy (:obj:`Policy`): Converged policy.
     """
-    cfg, policy, world_model, env_buffer, learner, collector, collector_env, evaluator, commander, tb_logger, resume_training = \
+    cfg, policy, world_model, env_buffer, learner, collector, collector_env, evaluator, commander, tb_logger = \
         mbrl_entry_setup(input_cfg, seed, env_setting, model)
 
     img_buffer = create_img_buffer(cfg, input_cfg, world_model, tb_logger)
 
     learner.call_hook('before_run')
-    if cfg.policy.learn.resume_training:
+    if cfg.policy.learn.get('resume_training', False):
         collector.envstep = learner.collector_envstep
 
     if cfg.policy.get('random_collect_size', 0) > 0:
@@ -200,11 +199,11 @@ def serial_pipeline_dream(
     Returns:
         - policy (:obj:`Policy`): Converged policy.
     """
-    cfg, policy, world_model, env_buffer, learner, collector, collector_env, evaluator, commander, tb_logger, resume_training = \
+    cfg, policy, world_model, env_buffer, learner, collector, collector_env, evaluator, commander, tb_logger = \
         mbrl_entry_setup(input_cfg, seed, env_setting, model)
 
     learner.call_hook('before_run')
-    if cfg.policy.learn.resume_training:
+    if cfg.policy.learn.get('resume_training', False):
         collector.envstep = learner.collector_envstep
 
     if cfg.policy.get('random_collect_size', 0) > 0:

ding/entry/serial_entry_ngu.py

@@ -54,7 +54,7 @@ def serial_pipeline_ngu(
         auto=True,
         create_cfg=create_cfg,
         save_cfg=True,
-        renew_dir=not cfg.policy.learn.resume_training
+        renew_dir=not cfg.policy.learn.get('resume_training', False)
     )
     # Create main components: env, policy
     if env_setting is None:
@@ -97,7 +97,7 @@ def serial_pipeline_ngu(
     # ==========
     # Learner's before_run hook.
     learner.call_hook('before_run')
-    if cfg.policy.learn.resume_training:
+    if cfg.policy.learn.get('resume_training', False):
         collector.envstep = learner.collector_envstep
 
     # Accumulate plenty of data at the beginning of training.

ding/entry/serial_entry_onpolicy.py

@@ -52,8 +52,9 @@ def serial_pipeline_onpolicy(
         auto=True,
         create_cfg=create_cfg,
         save_cfg=True,
-        renew_dir=not cfg.policy.learn.resume_training
+        renew_dir=not cfg.policy.learn.get('resume_training', False)
     )
+
     # Create main components: env, policy
     if env_setting is None:
         env_fn, collector_env_cfg, evaluator_env_cfg = get_vec_env_setting(cfg.env)
@@ -88,7 +89,7 @@ def serial_pipeline_onpolicy(
     # ==========
     # Learner's before_run hook.
     learner.call_hook('before_run')
-    if cfg.policy.learn.resume_training:
+    if cfg.policy.learn.get('resume_training', False):
         collector.envstep = learner.collector_envstep
 
     while True:

ding/entry/serial_entry_onpolicy_ppg.py

@@ -52,7 +52,7 @@ def serial_pipeline_onpolicy_ppg(
         auto=True,
         create_cfg=create_cfg,
         save_cfg=True,
-        renew_dir=not cfg.policy.learn.resume_training
+        renew_dir=not cfg.policy.learn.get('resume_training', False)
     )
     # Create main components: env, policy
     if env_setting is None:
@@ -88,7 +88,7 @@ def serial_pipeline_onpolicy_ppg(
     # ==========
     # Learner's before_run hook.
     learner.call_hook('before_run')
-    if cfg.policy.learn.resume_training:
+    if cfg.policy.learn.get('resume_training', False):
         collector.envstep = learner.collector_envstep
 
     while True:

dizoo/classic_control/cartpole/config/cartpole_a2c_config.py

@@ -21,7 +21,6 @@
             learning_rate=0.001,
             # (float) loss weight of the entropy regularization, the weight of policy network is set to 1
             entropy_weight=0.01,
-            resume_training=False,
         ),
         collect=dict(
             # (int) collect n_sample data, train model n_iteration times

dizoo/classic_control/cartpole/config/cartpole_pg_config.py

@@ -18,7 +18,6 @@
             batch_size=64,
             learning_rate=0.001,
             entropy_weight=0.001,
-            resume_training=False,
         ),
         collect=dict(n_episode=80, unroll_len=1, discount_factor=0.9),
         eval=dict(evaluator=dict(eval_freq=100, ), ),

dizoo/classic_control/cartpole/config/cartpole_ppo_stdim_config.py

@@ -37,7 +37,6 @@
             entropy_weight=0.01,
             clip_ratio=0.2,
             learner=dict(hook=dict(save_ckpt_after_iter=100)),
-            resume_training=False,
         ),
         collect=dict(
             n_sample=256,

dizoo/classic_control/cartpole/config/cartpole_ppopg_config.py

@@ -19,7 +19,6 @@
             batch_size=64,
             learning_rate=0.001,
             entropy_weight=0.001,
-            resume_training=False,
         ),
         collect=dict(n_episode=80, unroll_len=1, discount_factor=0.9, collector=dict(get_train_sample=True)),
         eval=dict(evaluator=dict(eval_freq=100, ), ),

dizoo/classic_control/pendulum/config/pendulum_ppo_config.py

@@ -35,7 +35,6 @@
             adv_norm=True,
             value_norm=True,
             ignore_done=True,
-            resume_training=False,
         ),
         collect=dict(
             n_sample=5000,

dizoo/petting_zoo/config/ptz_simple_spread_mappo_config.py

@@ -53,7 +53,6 @@
             grad_clip_type='clip_norm',
             grad_clip_value=10,
             ignore_done=False,
-            resume_training=False,
         ),
         collect=dict(
             n_sample=3200,