diff --git a/dizoo/gym_soccer/config/gym_soccer_pdqn_config.py b/dizoo/gym_soccer/config/gym_soccer_pdqn_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ffba22d217f7e4e622f99a3263ab016327c69d4
--- /dev/null
+++ b/dizoo/gym_soccer/config/gym_soccer_pdqn_config.py
@@ -0,0 +1,87 @@
+from easydict import EasyDict
+from ding.entry import serial_pipeline
+
+gym_soccer_pdqn_config = dict(
+    exp_name='gym_soccer_pdqn_seed1',
+    env=dict(
+        collector_env_num=8,
+        evaluator_env_num=3,
+        # (bool) Scale output action into legal range [-1, 1].
+        act_scale=True,
+        env_id='Soccer-v0',  # ['Soccer-v0', 'SoccerEmptyGoal-v0', 'SoccerAgainstKeeper-v0']
+        n_evaluator_episode=5,
+        stop_value=0.99,  # 1
+    ),
+    policy=dict(
+        cuda=True,
+        priority=False,
+        # (bool) Whether use Importance Sampling Weight to correct biased update. If True, priority must be True.
+        priority_IS_weight=False,
+        discount_factor=0.99,
+        nstep=1,
+        model=dict(
+            obs_shape=10,
+            action_shape=dict(
+                action_type_shape=3,
+                action_args_shape=5,
+            ),
+            # multi_pass=True,
+            # action_mask=[],
+        ),
+        learn=dict(
+            # (bool) Whether to use multi gpu
+            multi_gpu=False,
+            # How many updates(iterations) to train after collector's one collection.
+            # Bigger "update_per_collect" means bigger off-policy.
+            # collect data -> update policy-> collect data -> ...
+            update_per_collect=500,  # 100, 10,
+            batch_size=320,  # 32,
+            learning_rate_dis=3e-4,  # 1e-5, 3e-4, alpha
+            learning_rate_cont=3e-4,  # beta
+            target_theta=0.001,  # 0.005,
+            # cont_update_freq=10,
+            # disc_update_freq=10,
+            update_circle=10,
+        ),
+        # collect_mode config
+        collect=dict(
+            # (int) Only one of [n_sample, n_episode] shoule be set
+            n_sample=3200,  # 128,
+            # (int) Cut trajectories into pieces with length "unroll_len".
+            unroll_len=1,
+            noise_sigma=0.1,  # 0.05,
+            collector=dict(collect_print_freq=1000, ),
+        ),
+        eval=dict(evaluator=dict(eval_freq=1000, ), ),
+        # other config
+        other=dict(
+            # Epsilon greedy with decay.
+            eps=dict(
+                # (str) Decay type. Support ['exp', 'linear'].
+                type='exp',
+                start=1,  # 0.95,
+                end=0.1,  # 0.05,
+                # (int) Decay length(env step)
+                decay=int(1e5),
+            ),
+            replay_buffer=dict(replay_buffer_size=int(1e6), ),
+        ),
+    )
+)
+
+gym_soccer_pdqn_config = EasyDict(gym_soccer_pdqn_config)
+main_config = gym_soccer_pdqn_config
+
+gym_soccer_pdqn_create_config = dict(
+    env=dict(
+        type='gym_soccer',
+        import_names=['dizoo.gym_soccer.envs.gym_soccer_env'],
+    ),
+    env_manager=dict(type='subprocess'),
+    policy=dict(type='pdqn'),
+)
+gym_soccer_pdqn_create_config = EasyDict(gym_soccer_pdqn_create_config)
+create_config = gym_soccer_pdqn_create_config
+
+if __name__ == "__main__":
+    serial_pipeline([main_config, create_config], seed=1)
diff --git a/dizoo/gym_soccer/envs/gym_soccer_env.py b/dizoo/gym_soccer/envs/gym_soccer_env.py
index 75ddaf406344b245b999b6d652a7cb4c26dd6f73..2f4ac3ab8b303f3a5ea78f38ac194ed6e68e0997 100644
--- a/dizoo/gym_soccer/envs/gym_soccer_env.py
+++ b/dizoo/gym_soccer/envs/gym_soccer_env.py
@@ -10,6 +10,7 @@ from ding.envs.common.env_element import EnvElementInfo
 from ding.torch_utils import to_list, to_ndarray, to_tensor
 from ding.utils import ENV_REGISTRY
 from gym.utils import seeding
+import copy
 
 
 @ENV_REGISTRY.register('gym_soccer')
@@ -22,11 +23,11 @@ class GymSoccerEnv(BaseEnv):
         self._env_id = cfg.env_id
         assert self._env_id in self.default_env_id
         self._init_flag = False
-        self._replay_path = None
+        self._replay_path = './game_log'
 
     def reset(self) -> np.array:
         if not self._init_flag:
-            self._env = gym.make(self._env_id, replay_path=self._replay_path)
+            self._env = gym.make(self._env_id, replay_path=self._replay_path, port=self._cfg.port)  # TODO
             self._init_flag = True
         self._final_eval_reward = 0
         obs = self._env.reset()
@@ -124,3 +125,41 @@ class GymSoccerEnv(BaseEnv):
         if replay_path is None:
             replay_path = './game_log'
         self._replay_path = replay_path
+
+    def create_collector_env_cfg(cfg: dict) -> List[dict]:
+        """
+        Overview:
+            Return a list of all of the environment from input config.
+        Arguments:
+            - cfg (:obj:`Dict`) Env config, same config where ``self.__init__()`` takes arguments from
+        Returns:
+            - List of ``cfg`` including all of the collector env's config
+        """
+        cfg_list = []
+        collector_env_num = cfg.pop('collector_env_num')
+        port_pool = list(range(6000, 9999))
+        port_candidates = np.random.choice(port_pool, size=collector_env_num, replace=False)
+        for i in range(collector_env_num):
+            cfg_copy = copy.deepcopy(cfg)
+            cfg_copy.port = port_candidates[i]
+            cfg_list.append(cfg_copy)
+        return cfg_list
+
+    def create_evaluator_env_cfg(cfg: dict) -> List[dict]:
+        """
+        Overview:
+            Return a list of all of the environment from input config.
+        Arguments:
+            - cfg (:obj:`Dict`) Env config, same config where ``self.__init__()`` takes arguments from
+        Returns:
+            - List of ``cfg`` including all of the evaluator env's config
+        """
+        cfg_list = []
+        evaluator_env_num = cfg.pop('evaluator_env_num')
+        port_pool = list(range(6000, 9999))
+        port_candidates = np.random.choice(port_pool, size=evaluator_env_num, replace=False)
+        for i in range(evaluator_env_num):
+            cfg_copy = copy.deepcopy(cfg)
+            cfg_copy.port = port_candidates[i]
+            cfg_list.append(cfg_copy)
+        return cfg_list