Release 0.9

Main changes are detailed below:

New features -
* CARLA 0.7 simulator integration
* Human control of the game play
* Recording of human game play and storing / loading the replay buffer
* Behavioral cloning agent and presets
* Golden tests for several presets
* Selecting between deep / shallow image embedders
* Rendering through pygame (with some boost in performance)

API changes -
* Improved environment wrapper API
* Added an evaluate flag to allow convenient evaluation of existing checkpoints
* Improve frameskip definition in Gym

Bug fixes -
* Fixed loading of checkpoints for agents with more than one network
* Fixed the N Step Q learning agent python3 compatibility

utils.py

@@ -20,6 +20,7 @@ import os
 import numpy as np
 import threading
 from subprocess import call, Popen
+import signal
 
 killed_processes = []
 
@@ -54,9 +55,9 @@ class Enum(object):
 
 
 class RunPhase(Enum):
-    HEATUP = 0
-    TRAIN = 1
-    TEST = 2
+    HEATUP = "Heatup"
+    TRAIN = "Training"
+    TEST = "Testing"
 
 
 def list_all_classes_in_module(module):
@@ -292,3 +293,59 @@ def get_open_port():
     s.close()
     return port
 
+
+class timeout:
+    def __init__(self, seconds=1, error_message='Timeout'):
+        self.seconds = seconds
+        self.error_message = error_message
+
+    def _handle_timeout(self, signum, frame):
+        raise TimeoutError(self.error_message)
+
+    def __enter__(self):
+        signal.signal(signal.SIGALRM, self._handle_timeout)
+        signal.alarm(self.seconds)
+
+    def __exit__(self, type, value, traceback):
+        signal.alarm(0)
+
+
+def switch_axes_order(observation, from_type='channels_first', to_type='channels_last'):
+    """
+    transpose an observation axes from channels_first to channels_last or vice versa
+    :param observation: a numpy array
+    :param from_type: can be 'channels_first' or 'channels_last'
+    :param to_type: can be 'channels_first' or 'channels_last'
+    :return: a new observation with the requested axes order
+    """
+    if from_type == to_type or len(observation.shape) == 1:
+        return observation
+    assert 2 <= len(observation.shape) <= 3, 'num axes of an observation must be 2 for a vector or 3 for an image'
+    assert type(observation) == np.ndarray, 'observation must be a numpy array'
+    if len(observation.shape) == 3:
+        if from_type == 'channels_first' and to_type == 'channels_last':
+            return np.transpose(observation, (1, 2, 0))
+        elif from_type == 'channels_last' and to_type == 'channels_first':
+            return np.transpose(observation, (2, 0, 1))
+    else:
+        return np.transpose(observation, (1, 0))
+
+
+def stack_observation(curr_stack, observation, stack_size):
+    """
+    Adds a new observation to an existing stack of observations from previous time-steps.
+    :param curr_stack: The current observations stack.
+    :param observation: The new observation
+    :param stack_size: The required stack size
+    :return: The updated observation stack
+    """
+
+    if curr_stack == []:
+        # starting an episode
+        curr_stack = np.vstack(np.expand_dims([observation] * stack_size, 0))
+        curr_stack = switch_axes_order(curr_stack, from_type='channels_first', to_type='channels_last')
+    else:
+        curr_stack = np.append(curr_stack, np.expand_dims(np.squeeze(observation), axis=-1), axis=-1)
+        curr_stack = np.delete(curr_stack, 0, -1)
+
+    return curr_stack