diff --git a/test/discrete/test_pdqn.py b/test/discrete/test_pdqn.py
index 8450c48..70bfdbb 100644
--- a/test/discrete/test_pdqn.py
+++ b/test/discrete/test_pdqn.py
@@ -74,7 +74,8 @@ def test_pdqn(args=get_args()):
     # collector
     if args.prioritized_replay > 0:
         buf = PrioritizedReplayBuffer(
-            args.buffer_size, alpha=args.alpha, beta=args.alpha)
+            args.buffer_size, alpha=args.alpha,
+            beta=args.alpha, repeat_sample=True)
     else:
         buf = ReplayBuffer(args.buffer_size)
     train_collector = Collector(
diff --git a/tianshou/data/buffer.py b/tianshou/data/buffer.py
index cd05393..962dc7e 100644
--- a/tianshou/data/buffer.py
+++ b/tianshou/data/buffer.py
@@ -340,6 +340,7 @@ class PrioritizedReplayBuffer(ReplayBuffer):
     :param float alpha: the prioritization exponent.
     :param float beta: the importance sample soft coefficient.
     :param str mode: defaults to ``weight``.
+    :param bool replace: whether to sample with replacement
 
     .. seealso::
 
@@ -348,7 +349,8 @@ class PrioritizedReplayBuffer(ReplayBuffer):
     """
 
     def __init__(self, size: int, alpha: float, beta: float,
-                 mode: str = 'weight', **kwargs) -> None:
+                 mode: str = 'weight',
+                 replace: bool = False, **kwargs) -> None:
         if mode != 'weight':
             raise NotImplementedError
         super().__init__(size, **kwargs)
@@ -358,6 +360,7 @@ class PrioritizedReplayBuffer(ReplayBuffer):
         self.weight = np.zeros(size, dtype=np.float64)
         self._amortization_freq = 50
         self._amortization_counter = 0
+        self._replace = replace
 
     def add(self,
             obs: Union[dict, np.ndarray],
@@ -377,6 +380,14 @@ class PrioritizedReplayBuffer(ReplayBuffer):
         super().add(obs, act, rew, done, obs_next, info, policy)
         self._check_weight_sum()
 
+    @property
+    def replace(self):
+        return self._replace
+
+    @replace.setter
+    def replace(self, v: bool):
+        self._replace = v
+
     def sample(self, batch_size: int,
                importance_sample: bool = True
                ) -> Tuple[Batch, np.ndarray]:
@@ -391,7 +402,7 @@ class PrioritizedReplayBuffer(ReplayBuffer):
             indice = np.random.choice(
                 self._size, batch_size,
                 p=(self.weight / self.weight.sum())[:self._size],
-                replace=False)
+                replace=self._replace)
             # self._weight_sum is not work for the accuracy issue
             # p=(self.weight/self._weight_sum)[:self._size], replace=False)
         elif batch_size == 0:
@@ -402,7 +413,9 @@ class PrioritizedReplayBuffer(ReplayBuffer):
         else:
             # if batch_size larger than len(self),
             # it will lead to a bug in update weight
-            raise ValueError("batch_size should be less than len(self)")
+            raise ValueError(
+                "batch_size should be less than len(self), \
+                    or set replace=False")
         batch = self[indice]
         if importance_sample:
             impt_weight = Batch(
@@ -424,6 +437,14 @@ class PrioritizedReplayBuffer(ReplayBuffer):
         :param np.ndarray indice: indice you want to update weight
         :param np.ndarray new_weight: new priority weight you wangt to update
         """
+        if self._replace:
+            if isinstance(indice, slice):
+                # convert slice to ndarray
+                indice = np.arange(indice.stop)[indice]
+            # remove the same values in indice
+            indice, unique_indice = np.unique(
+                indice, return_index=True)
+            new_weight = new_weight[unique_indice]
         self._weight_sum += np.power(np.abs(new_weight), self._alpha).sum() \
             - self.weight[indice].sum()
         self.weight[indice] = np.power(np.abs(new_weight), self._alpha)