diff --git a/ding/model/wrapper/model_wrappers.py b/ding/model/wrapper/model_wrappers.py
index 31718f07d765b74ee1114627e1ed895d66f80278..11ecb8b97a2206c88eee5d05ceaca0d5bcd2db34 100644
--- a/ding/model/wrapper/model_wrappers.py
+++ b/ding/model/wrapper/model_wrappers.py
@@ -338,7 +338,7 @@ class EpsGreedyMultinomialSampleWrapper(IModelWrapper):
         for i, l in enumerate(logit):
             if np.random.random() > eps:
                 prob = torch.softmax(output['logit'] / alpha, dim=-1)
-                prob = prob / torch.sum(prob, 1, keepdims=True)
+                prob = prob / torch.sum(prob, 1, keepdim=True)
                 pi_action = torch.zeros(prob.shape)
                 pi_action = Categorical(prob)
                 pi_action = pi_action.sample()
@@ -386,7 +386,7 @@ class HybridEpsGreedyMultinomialSampleWrapper(IModelWrapper):
         for i, l in enumerate(logit):
             if np.random.random() > eps:
                 prob = torch.softmax(l, dim=-1)
-                prob = prob / torch.sum(prob, 1, keepdims=True)
+                prob = prob / torch.sum(prob, 1, keepdim=True)
                 pi_action = Categorical(prob)
                 pi_action = pi_action.sample()
                 action.append(pi_action)
@@ -441,51 +441,6 @@ class EpsGreedySampleNGUWrapper(IModelWrapper):
         return output
 
 
-class EpsGreedySampleWrapperSql(IModelWrapper):
-    r"""
-    Overview:
-        Epsilon greedy sampler coupled with multinomial sample used in collector_model
-        to help balance exploration and exploitation.
-    Interfaces:
-        register
-    """
-
-    def forward(self, *args, **kwargs):
-        eps = kwargs.pop('eps')
-        alpha = kwargs.pop('alpha')
-        output = self._model.forward(*args, **kwargs)
-        assert isinstance(output, dict), "model output must be dict, but find {}".format(type(output))
-        logit = output['logit']
-        assert isinstance(logit, torch.Tensor) or isinstance(logit, list)
-        if isinstance(logit, torch.Tensor):
-            logit = [logit]
-        if 'action_mask' in output:
-            mask = output['action_mask']
-            if isinstance(mask, torch.Tensor):
-                mask = [mask]
-            logit = [l.sub_(1e8 * (1 - m)) for l, m in zip(logit, mask)]
-        else:
-            mask = None
-        action = []
-        for i, l in enumerate(logit):
-            if np.random.random() > eps:
-                prob = torch.softmax(output['logit'] / alpha, dim=-1)
-                prob = prob / torch.sum(prob, 1, keepdims=True)
-                pi_action = torch.zeros(prob.shape)
-                pi_action = Categorical(prob)
-                pi_action = pi_action.sample()
-                action.append(pi_action)
-            else:
-                if mask:
-                    action.append(sample_action(prob=mask[i].float()))
-                else:
-                    action.append(torch.randint(0, l.shape[-1], size=l.shape[:-1]))
-        if len(action) == 1:
-            action, logit = action[0], logit[0]
-        output['action'] = action
-        return output
-
-
 class ActionNoiseWrapper(IModelWrapper):
     r"""
     Overview:
@@ -629,7 +584,6 @@ wrapper_name_map = {
     'hybrid_argmax_sample': HybridArgmaxSampleWrapper,
     'eps_greedy_sample': EpsGreedySampleWrapper,
     'eps_greedy_sample_ngu': EpsGreedySampleNGUWrapper,
-    'eps_greedy_sample_sql': EpsGreedySampleWrapperSql,
     'eps_greedy_multinomial_sample': EpsGreedyMultinomialSampleWrapper,
     'hybrid_eps_greedy_sample': HybridEpsGreedySampleWrapper,
     'hybrid_eps_greedy_multinomial_sample': HybridEpsGreedyMultinomialSampleWrapper,