diff --git a/pybrain/datasets/sequential.py b/pybrain/datasets/sequential.py
index 9c4f25da1..03b21447d 100644
--- a/pybrain/datasets/sequential.py
+++ b/pybrain/datasets/sequential.py
@@ -42,7 +42,7 @@ def _getSequenceField(self, index, field):
         seq = ravel(self.getField('sequence_index'))
         if len(seq) == index + 1:
             # user wants to access the last sequence, return until end of data
-            return self.getField(field)[seq[index]:]
+            return self.getField(field)[int(seq[index]):]
         if len(seq) < index + 1:
             # sequence index beyond number of sequences. raise exception
             raise IndexError('sequence does not exist.')
diff --git a/pybrain/rl/learners/valuebased/interface.py b/pybrain/rl/learners/valuebased/interface.py
index f74beb617..c4a6ae1c0 100644
--- a/pybrain/rl/learners/valuebased/interface.py
+++ b/pybrain/rl/learners/valuebased/interface.py
@@ -50,7 +50,7 @@ def _forwardImplementation(self, inbuf, outbuf):
 
     def getMaxAction(self, state):
         """ Return the action with the maximal value for the given state. """
-        values = self.params.reshape(self.numRows, self.numColumns)[state, :].flatten()
+        values = self.params.reshape(self.numRows, self.numColumns)[int(state), :].flatten()
         action = where(values == max(values))[0]
         action = choice(action)
         return action
@@ -91,4 +91,4 @@ def getActionValues(self, state):
         return values
 
     def getValue(self, state, action):
-        return self.network.activate(r_[state, one_to_n(action, self.numActions)])
\ No newline at end of file
+        return self.network.activate(r_[state, one_to_n(action, self.numActions)])