remove variable observation_space since it is not really used anywhere and...

remove variable observation_space since it is not really used anywhere and adjust examples in doc to match the code

docs/tutorials/01_gettingstarted.rst

@@ -160,7 +160,7 @@ Once we are set with the environment we can load our preferred agent from either
 
 .. code-block:: python
 
-    agent = RandomAgent(env.action_space, env.observation_space)
+    agent = RandomAgent(state_size, action_size)
 
 We start every trial by resetting the environment
 

docs/tutorials/02_observationbuilder.rst

@@ -18,7 +18,7 @@ base class and must implement two methods, :code:`reset(self)` and :code:`get(se
 
 .. _`flatland.core.env_observation_builder.ObservationBuilder` : https://gitlab.aicrowd.com/flatland/flatland/blob/master/flatland/core/env_observation_builder.py#L13
 
-Below is a simple example that returns observation vectors of size :code:`observation_space = 5` featuring only the ID (handle) of the agent whose
+Below is a simple example that returns observation vectors of size 5 featuring only the ID (handle) of the agent whose
 observation vector is being computed:
 
 .. code-block:: python
@@ -28,14 +28,12 @@ observation vector is being computed:
         Simplest observation builder. The object returns observation vectors with 5 identical components,
         all equal to the ID of the respective agent.
         """
-        def __init__(self):
-            self.observation_space = [5]
 
         def reset(self):
             return
 
         def get(self, handle):
-            observation = handle * np.ones((self.observation_space[0],))
+            observation = handle * np.ones(5)
             return observation
 
 We can pass an instance of our custom observation builder :code:`SimpleObs` to the :code:`RailEnv` creator as follows:
@@ -85,7 +83,6 @@ Note that this simple strategy fails when multiple agents are present, as each a
             super().__init__(max_depth=0)
             # We set max_depth=0 in because we only need to look at the current
             # position of the agent to decide what direction is shortest.
-            self.observation_space = [3]
 
         def reset(self):
             # Recompute the distance map, if the environment has changed.
@@ -189,7 +186,6 @@ In contrast to the previous examples we also implement the :code:`def get_many(s
 
         def __init__(self, predictor):
             super().__init__(max_depth=0)
-            self.observation_space = [10]
             self.predictor = predictor
 
         def reset(self):

examples/custom_observation_example_01_SimpleObs.py

@@ -18,7 +18,6 @@ class SimpleObs(ObservationBuilder):
 
     def __init__(self):
         super().__init__()
-        self.observation_space = [5]
 
     def reset(self):
         return

examples/custom_observation_example_02_SingleAgentNavigationObs.py

@@ -28,7 +28,6 @@ class SingleAgentNavigationObs(ObservationBuilder):
 
     def __init__(self):
         super().__init__()
-        self.observation_space = [3]
 
     def reset(self):
         pass

examples/custom_observation_example_03_ObservePredictions.py

@@ -28,7 +28,6 @@ class ObservePredictions(ObservationBuilder):
 
     def __init__(self, predictor):
         super().__init__()
-        self.observation_space = [10]
         self.predictor = predictor
 
     def reset(self):

examples/debugging_example_DELETE.py

@@ -25,7 +25,6 @@ class SingleAgentNavigationObs(ObservationBuilder):
 
     def __init__(self):
         super().__init__()
-        self.observation_space = [3]
 
     def reset(self):
         pass

flatland/core/env.py

@@ -11,7 +11,6 @@ class Environment:
 
     Derived environments should implement the following attributes:
         action_space: tuple with the dimensions of the actions to be passed to the step method
-        observation_space: tuple with the dimensions of the observations returned by reset and step
 
     Agents are identified by agent ids (handles).
     Examples:
@@ -46,7 +45,6 @@ class Environment:
 
     def __init__(self):
         self.action_space = ()
-        self.observation_space = ()
         pass
 
     def reset(self):

flatland/core/env_observation_builder.py

@@ -18,13 +18,9 @@ from flatland.core.env import Environment
 class ObservationBuilder:
     """
     ObservationBuilder base class.
-
-    Derived objects must implement and `observation_space` attribute as a tuple with the dimensions of the returned
-    observations.
     """
 
     def __init__(self):
-        self.observation_space = ()
         self.env = None
 
     def set_env(self, env: Environment):

flatland/envs/observations.py

@@ -42,13 +42,6 @@ class TreeObsForRailEnv(ObservationBuilder):
         super().__init__()
         self.max_depth = max_depth
         self.observation_dim = 11
-        # Compute the size of the returned observation vector
-        size = 0
-        pow4 = 1
-        for i in range(self.max_depth + 1):
-            size += pow4
-            pow4 *= 4
-        self.observation_space = [size * self.observation_dim]
         self.location_has_agent = {}
         self.location_has_agent_direction = {}
         self.predictor = predictor
@@ -508,12 +501,10 @@ class GlobalObsForRailEnv(ObservationBuilder):
     """
 
     def __init__(self):
-        self.observation_space = ()
         super(GlobalObsForRailEnv, self).__init__()
 
     def set_env(self, env: Environment):
         super().set_env(env)
-        self.observation_space = [4, self.env.height, self.env.width]
 
     def reset(self):
         self.rail_obs = np.zeros((self.env.height, self.env.width, 16))

flatland/envs/rail_env.py

@@ -178,7 +178,6 @@ class RailEnv(Environment):
         self.distance_map = DistanceMap(self.agents, self.height, self.width)
 
         self.action_space = [1]
-        self.observation_space = self.obs_builder.observation_space  # updated on resets?
 
         # Stochastic train malfunctioning parameters
         if stochastic_data is not None:
@@ -288,7 +287,6 @@ class RailEnv(Environment):
 
         # Reset the state of the observation builder with the new environment
         self.obs_builder.reset()
-        self.observation_space = self.obs_builder.observation_space  # <-- change on reset?
         self.distance_map.reset(self.agents, self.rail)
 
         # Return the new observation vectors for each agent

tests/test_flatland_malfunction.py

@@ -22,7 +22,6 @@ class SingleAgentNavigationObs(ObservationBuilder):
 
     def __init__(self):
         super().__init__()
-        self.observation_space = [3]
 
     def reset(self):
         pass