Added default reward_function for cont. envs; remove bug in ImageCont…

…inuous;

example.py

@@ -127,7 +127,7 @@ def discrete_environment_image_representations_example():
     augmented_state_dict = env.get_augmented_state()
     next_state = augmented_state_dict["curr_state"]  # Underlying MDP state holds
     # the current discrete state.
-    print("sars', done =", state, action, reward, next_state, done)
+    print("sars', done, image shape =", state, action, reward, next_state, done, next_state_image.shape)
 
     env.close()
 
@@ -175,7 +175,7 @@ def discrete_environment_diameter_image_representations_example():
     augmented_state_dict = env.get_augmented_state()
     next_state = augmented_state_dict["curr_state"]  # Underlying MDP state holds
     # the current discrete state.
-    print("sars', done =", state, action, reward, next_state, done)
+    print("sars', done, shape =", state, action, reward, next_state, done, next_state_image.shape)
 
     env.close()
 
@@ -262,7 +262,7 @@ def continuous_environment_example_move_to_a_point_irrelevant_image():
     augmented_state_dict = env.get_augmented_state()
     next_state = augmented_state_dict["curr_state"].copy()  # Underlying MDP state holds
     # the current continuous state.
-    print("sars', done =", state, action, reward, next_state, done)
+    print("sars', done, image shape =", state, action, reward, next_state, done, next_state_image.shape)
 
     env.close()
 
@@ -388,7 +388,7 @@ def grid_environment_image_representations_example():
         action = actions[i]
         next_obs, reward, done, trunc, info = env.step(action)
         next_state = env.get_augmented_state()["augmented_state"][-1]
-        print("sars', done =", state, action, reward, next_state, done)
+        print("sars', done, image shape =", state, action, reward, next_state, done, next_obs.shape)
         state = next_state
 
     env.reset()[0]

mdp_playground/envs/rl_toy_env.py

@@ -345,6 +345,7 @@ def __init__(self, **config):
                 # if config["state_space_type"] == "discrete":
                 #     assert "init_state_dist" in config
 
+        # Common defaults for all types of environments:
         if "terminal_state_density" not in config:
             self.terminal_state_density = 0.25
         else:
@@ -483,6 +484,7 @@ def __init__(self, **config):
                 else:
                     self.image_scale_range = config["image_scale_range"]
 
+        # Defaults for the individual environment types:
         if config["state_space_type"] == "discrete":
             if "reward_dist" not in config:
                 self.reward_dist = None
@@ -498,6 +500,11 @@ def __init__(self, **config):
             # if not self.use_custom_mdp:
             self.state_space_dim = config["state_space_dim"]
 
+            # ##TODO Do something to dismbiguate the Python function reward_function from the 
+            # choice of reward_function below.
+            if "reward_function" not in config:
+                config["reward_function"] = "move_to_a_point"
+
             if "transition_dynamics_order" not in config:
                 self.dynamics_order = 1
             else:
@@ -548,8 +555,9 @@ def __init__(self, **config):
             self.repeats_in_sequences = config["repeats_in_sequences"]
 
 
+        # ##TODO Move these to the individual env types' defaults section above?
         if config["state_space_type"] == "discrete":
-            self.dtype_s = np.int64 if "dtype_s" not in config else config["dtype_s"]
+            self.dtype_s = np.int32 if "dtype_s" not in config else config["dtype_s"]
             if self.irrelevant_features:
                 assert (
                     len(config["action_space_size"]) == 2
@@ -589,7 +597,7 @@ def __init__(self, **config):
 
         # Set the dtype for the observation space:
         if self.image_representations:
-            self.dtype_o = np.float32 if "dtype_o" not in config else config["dtype_o"]
+            self.dtype_o = np.uint8 if "dtype_o" not in config else config["dtype_o"]
         else:
             self.dtype_o = self.dtype_s if "dtype_o" not in config else config["dtype_o"]
 

mdp_playground/spaces/image_continuous.py

@@ -24,6 +24,7 @@ def __init__(
         term_spaces=None,
         width=100,
         height=100,
+        num_channels=3,
         circle_radius=5,
         target_point=None,
         relevant_indices=[0, 1],
@@ -43,6 +44,8 @@ def __init__(
             The width of the image
         height : int
             The height of the image
+        num_channels : int
+            The number of channels in the image  ###TODO: Support for 1 channel; unify with ImageMultiDiscrete
         circle_radius : int
             The radius of the circle which represents the agent and target point
         target_point : np.array
@@ -60,6 +63,7 @@ def __init__(
         assert (self.feature_space.low != -np.inf).any()
         self.width = width
         self.height = height
+        self.num_channels = num_channels
         # Warn if resolution is too low?
         self.circle_radius = circle_radius
         self.target_point = target_point
@@ -99,7 +103,7 @@ def __init__(
 
         # Shape has 1 appended for Ray Rllib to be compatible IIRC
         super(ImageContinuous, self).__init__(
-            shape=(width, height, 1), dtype=dtype, low=0, high=255
+            shape=(width, height, num_channels), dtype=dtype, low=0, high=255
         )
         super(ImageContinuous, self).seed(seed=seed)
 
@@ -117,10 +121,10 @@ def generate_image(self, position, relevant=True):
 
         """
         # Use RGB
-        image_ = Image.new("RGB", (self.width, self.height), color=self.bg_colour)
-        # Use L for black and white 8-bit pixels instead of RGB in case not
-        # using custom images
-        # image_ = Image.new("L", (self.width, self.height))
+        if self.num_channels == 3:
+            image_ = Image.new("RGB", (self.width, self.height), color=self.bg_colour)
+        elif self.num_channels == 1:
+            image_ = Image.new("L", (self.width, self.height), color=self.bg_colour)
         draw = ImageDraw.Draw(image_)
 
         # Draw in decreasing order of importance:
@@ -239,7 +243,7 @@ def contains(self, x):
         if x.shape == (
             self.width,
             self.height,
-            1,
+            self.num_channels,
         ):  # TODO compare each pixel for all possible images?
             return True