feature(nyz): add example and polish style

ding/example/mappo.py

@@ -0,0 +1,45 @@
+import gym
+from ditk import logging
+from ding.model import MAVAC
+from ding.policy import PPOPolicy
+from ding.envs import DingEnvWrapper, SubprocessEnvManagerV2
+from ding.data import DequeBuffer
+from ding.config import compile_config
+from ding.framework import task, ding_init
+from ding.framework.context import OnlineRLContext
+from ding.framework.middleware import multistep_trainer, StepCollector, interaction_evaluator, CkptSaver, \
+    gae_estimator, online_logger, termination_checker
+from ding.utils import set_pkg_seed
+from dizoo.petting_zoo.config.ptz_simple_spread_mappo_config import main_config, create_config
+from dizoo.petting_zoo.envs.petting_zoo_simple_spread_env import PettingZooEnv
+
+
+def main():
+    logging.getLogger().setLevel(logging.INFO)
+    cfg = compile_config(main_config, create_cfg=create_config, auto=True)
+    ding_init(cfg)
+    with task.start(async_mode=False, ctx=OnlineRLContext()):
+        collector_env = SubprocessEnvManagerV2(
+            env_fn=[lambda: PettingZooEnv(cfg.env) for _ in range(cfg.env.collector_env_num)], cfg=cfg.env.manager
+        )
+        evaluator_env = SubprocessEnvManagerV2(
+            env_fn=[lambda: PettingZooEnv(cfg.env) for _ in range(cfg.env.evaluator_env_num)], cfg=cfg.env.manager
+        )
+
+        set_pkg_seed(cfg.seed, use_cuda=cfg.policy.cuda)
+
+        model = MAVAC(**cfg.policy.model)
+        policy = PPOPolicy(cfg.policy, model=model)
+
+        task.use(interaction_evaluator(cfg, policy.eval_mode, evaluator_env))
+        task.use(StepCollector(cfg, policy.collect_mode, collector_env))
+        task.use(gae_estimator(cfg, policy.collect_mode))
+        task.use(multistep_trainer(policy.learn_mode, log_freq=100))
+        task.use(CkptSaver(policy, cfg.exp_name, train_freq=1000))
+        task.use(online_logger(train_show_freq=10))
+        task.use(termination_checker(max_env_step=int(1e6)))
+        task.run()
+
+
+if __name__ == "__main__":
+    main()

ding/example/masac.py

@@ -0,0 +1,49 @@
+import gym
+from ditk import logging
+from ding.model import MAQAC
+from ding.policy import SACDiscretePolicy
+from ding.envs import DingEnvWrapper, BaseEnvManagerV2
+from ding.data import DequeBuffer
+from ding.config import compile_config
+from ding.framework import task, ding_init
+from ding.framework.context import OnlineRLContext
+from ding.framework.middleware import OffPolicyLearner, StepCollector, interaction_evaluator, CkptSaver, \
+    data_pusher, online_logger, termination_checker, eps_greedy_handler
+from ding.utils import set_pkg_seed
+from dizoo.petting_zoo.config.ptz_simple_spread_masac_config import main_config, create_config
+from dizoo.petting_zoo.envs.petting_zoo_simple_spread_env import PettingZooEnv
+
+
+def main():
+    logging.getLogger().setLevel(logging.INFO)
+    cfg = compile_config(main_config, create_cfg=create_config, auto=True)
+    ding_init(cfg)
+    with task.start(async_mode=False, ctx=OnlineRLContext()):
+        collector_env = BaseEnvManagerV2(
+            env_fn=[lambda: PettingZooEnv(cfg.env) for _ in range(cfg.env.collector_env_num)], cfg=cfg.env.manager
+        )
+        evaluator_env = BaseEnvManagerV2(
+            env_fn=[lambda: PettingZooEnv(cfg.env) for _ in range(cfg.env.evaluator_env_num)], cfg=cfg.env.manager
+        )
+
+        set_pkg_seed(cfg.seed, use_cuda=cfg.policy.cuda)
+
+        model = MAQAC(**cfg.policy.model)
+        buffer_ = DequeBuffer(size=cfg.policy.other.replay_buffer.replay_buffer_size)
+        policy = SACDiscretePolicy(cfg.policy, model=model)
+
+        task.use(interaction_evaluator(cfg, policy.eval_mode, evaluator_env))
+        task.use(eps_greedy_handler(cfg))
+        task.use(
+            StepCollector(cfg, policy.collect_mode, collector_env, random_collect_size=cfg.policy.random_collect_size)
+        )
+        task.use(data_pusher(cfg, buffer_))
+        task.use(OffPolicyLearner(cfg, policy.learn_mode, buffer_, log_freq=100))
+        task.use(CkptSaver(policy, cfg.exp_name, train_freq=1000))
+        task.use(online_logger(train_show_freq=10))
+        task.use(termination_checker(max_env_step=int(1e6)))
+        task.run()
+
+
+if __name__ == "__main__":
+    main()

ding/torch_utils/network/nn_module.py

@@ -174,8 +174,10 @@ def conv2d_block(
         elif norm_type in ['BN', 'IN', 'SyncBN']:
             block.append(build_normalization(norm_type, dim=2)(out_channels))
         else:
-            raise KeyError("Invalid value in norm_type: {}. The valid norm_type are "
-                           "BN, LN, IN, GN and SyncBN.".format(norm_type))
+            raise KeyError(
+                "Invalid value in norm_type: {}. The valid norm_type are "
+                "BN, LN, IN, GN and SyncBN.".format(norm_type)
+            )
 
     if activation is not None:
         block.append(activation)