Merge pull request #17 from pseudo-rnd-thoughts/np.bool

Change np.bool to bool

examples/models/mx_model/a2c.py

@@ -97,7 +97,7 @@ def __init__(self, env, handle, name, eval_obs=None,
         self.feature_buf = np.empty((1,) + self.feature_space)
         self.action_buf = np.empty(1, dtype=np.int32)
         self.advantage_buf, self.value_buf = np.empty(1), np.empty(1)
-        self.terminal_buf = np.empty(1, dtype=np.bool)
+        self.terminal_buf = np.empty(1, dtype=bool)
 
         # print("parameters", self.model.get_params())
         # mx.viz.plot_network(self.output).view()

examples/models/mx_model/dqn.py

@@ -126,7 +126,7 @@ def __init__(self, env, handle, name,
         self.replay_buf_feature  = ReplayBuffer(shape=(memory_size,) + self.feature_space)
         self.replay_buf_action   = ReplayBuffer(shape=(memory_size,), dtype=np.int32)
         self.replay_buf_reward   = ReplayBuffer(shape=(memory_size,))
-        self.replay_buf_terminal = ReplayBuffer(shape=(memory_size,), dtype=np.bool)
+        self.replay_buf_terminal = ReplayBuffer(shape=(memory_size,), dtype=bool)
         self.replay_buf_mask     = ReplayBuffer(shape=(memory_size,))
         # if mask[i] == 0, then the item is used for padding, not for training
 
@@ -259,7 +259,7 @@ def _add_to_replay_buffer(self, sample_buffer):
             m = len(r)
 
             mask = np.ones((m,))
-            terminal = np.zeros((m,), dtype=np.bool)
+            terminal = np.zeros((m,), dtype=bool)
             if episode.terminal:
                 terminal[-1] = True
             else:

examples/models/tf_model/dqn.py

@@ -144,7 +144,7 @@ def out_action(qvalues):
         self.replay_buf_feature  = ReplayBuffer(shape=(memory_size,) + self.feature_space)
         self.replay_buf_action   = ReplayBuffer(shape=(memory_size,), dtype=np.int32)
         self.replay_buf_reward   = ReplayBuffer(shape=(memory_size,))
-        self.replay_buf_terminal = ReplayBuffer(shape=(memory_size,), dtype=np.bool)
+        self.replay_buf_terminal = ReplayBuffer(shape=(memory_size,), dtype=bool)
         self.replay_buf_mask     = ReplayBuffer(shape=(memory_size,))
         # if mask[i] == 0, then the item is used for padding, not for training
 
@@ -256,7 +256,7 @@ def _add_to_replay_buffer(self, sample_buffer):
             m = len(r)
 
             mask = np.ones((m,))
-            terminal = np.zeros((m,), dtype=np.bool)
+            terminal = np.zeros((m,), dtype=bool)
             if episode.terminal:
                 terminal[-1] = True
             else:

examples/models/tf_model/drqn.py

@@ -135,7 +135,7 @@ def __init__(self, env, handle, name,
         self.view_buf = np.empty((1,) + self.view_space)
         self.feature_buf = np.empty((1,) + self.feature_space)
         self.action_buf, self.reward_buf = np.empty(1, dtype=np.int32), np.empty(1)
-        self.terminal_buf = np.empty(1, dtype=np.bool)
+        self.terminal_buf = np.empty(1, dtype=bool)
 
     def _create_network(self, input_view, input_feature, reuse=None):
         """define computation graph of network
@@ -285,7 +285,7 @@ def _add_to_replay_buffer(self, sample_buffer):
             m = len(r)
 
             mask = np.ones((m,))
-            terminal = np.zeros((m,), dtype=np.bool)
+            terminal = np.zeros((m,), dtype=bool)
             if episode.terminal:
                 terminal[-1] = True
             else:
@@ -335,7 +335,7 @@ def train(self, sample_buffer, print_every=500):
         batch_feature = np.zeros((max_+1,) + self.feature_space, dtype=np.float32)
         batch_action = np.zeros((max_,), dtype=np.int32)
         batch_reward = np.zeros((max_,), dtype=np.float32)
-        batch_terminal = np.zeros((max_,), dtype=np.bool)
+        batch_terminal = np.zeros((max_,), dtype=bool)
         batch_mask = np.zeros((max_,), dtype=np.float32)
 
         # calc batch number
@@ -443,10 +443,10 @@ def train_keep_hidden(self, sample_buffer, print_every=500):
         batch_feature  = np.zeros((max_+1,) + self.feature_space, dtype=np.float32)
         batch_action   = np.zeros((max_,), dtype=np.int32)
         batch_reward   = np.zeros((max_,), dtype=np.float32)
-        batch_terminal = np.zeros((max_,), dtype=np.bool)
+        batch_terminal = np.zeros((max_,), dtype=bool)
         batch_mask     = np.zeros((max_,), dtype=np.float32)
         batch_hidden   = np.zeros((batch_size, self.state_size), dtype=np.float32)
-        batch_pick     = np.zeros((batch_size, max_len), dtype=np.bool)
+        batch_pick     = np.zeros((batch_size, max_len), dtype=bool)
         pick_buffer    = np.arange(max_, dtype=np.int32)
 
         print("batches: %d  add: %d  replay_len: %d, %d/%d" %
@@ -486,7 +486,7 @@ def train_keep_hidden(self, sample_buffer, print_every=500):
                     rows.append(row)
             n_rows = len(rows)
 
-            batch_reset = np.zeros((train_length, batch_size), dtype=np.bool)
+            batch_reset = np.zeros((train_length, batch_size), dtype=bool)
             batch_mask[:] = 0
 
             # copy from replay buffer to batch buffer

magent/gridworld.py

@@ -353,7 +353,7 @@ def get_alive(self, handle):
             whether the agents are alive
         """
         n = self.get_num(handle)
-        buf = np.empty((n,), dtype=np.bool)
+        buf = np.empty((n,), dtype=bool)
         _LIB.env_get_info(self.game, handle, b"alive",
                           buf.ctypes.data_as(ctypes.POINTER(ctypes.c_bool)))
         return buf