Using state vector in env

overcooked_simulator/game_content/item_info.yaml

@@ -70,6 +70,7 @@ Cheese:
 Sausage:
   type: Ingredient
 
+# Chopped things
 ChoppedTomato:
   type: Ingredient
   needs: [ Tomato ]

overcooked_simulator/game_content/layouts/rl.layout

-#X##
-T__#
-U__P
-##W#
+#X###
+T___#
+#___#
+U___P
+##W##

overcooked_simulator/gym_env.py

 import json
 import time
+from copy import copy, deepcopy
 from datetime import timedelta
 from enum import Enum
 from pathlib import Path
@@ -116,12 +117,25 @@ def get_env_action(player_id, simple_action, duration):
             print("FAIL", simple_action)
 
 
-environment_config_path: Path = ROOT_DIR / "game_content" / "environment_config_rl.yaml"
-item_info_path: Path = ROOT_DIR / "game_content" / "item_info_rl.yaml"
+
+environment_config_path = ROOT_DIR / "game_content" / "environment_config_rl.yaml"
 layout_path: Path = ROOT_DIR / "game_content" / "layouts" / "rl.layout"
+item_info_path = ROOT_DIR / "game_content" / "item_info_rl.yaml"
+with open(item_info_path, "r") as file:
+    item_info = file.read()
+with open(layout_path, "r") as file:
+    layout = file.read()
+with open(environment_config_path, "r") as file:
+    environment_config = file.read()
 with open(ROOT_DIR / "gui_2d_vis" / "visualization_rl.yaml", "r") as file:
     visualization_config = yaml.safe_load(file)
 
+vanilla_env: Environment = Environment(
+            env_config=environment_config,
+            layout_config=layout,
+            item_info=item_info,
+            as_files=False
+        )
 
 class EnvGymWrapper(Env):
     """Should enable this:
@@ -135,12 +149,7 @@ class EnvGymWrapper(Env):
 
         self.gridsize = 20
 
-        self.env: Environment = Environment(
-            env_config=environment_config_path,
-            layout_config=layout_path,
-            item_info=item_info_path,
-            as_files=True,
-        )
+        self.env = deepcopy(vanilla_env)
 
         self.visualizer: Visualizer = Visualizer(config=visualization_config)
         self.player_name = str(0)
@@ -160,9 +169,10 @@ class EnvGymWrapper(Env):
         self.action_space = spaces.Discrete(len(self.action_space_map))
         # Example for using image as input (channel-first; channel-last also works):
 
-        dummy_obs = self.get_env_img(self.gridsize)
+        # dummy_obs = self.get_env_img(self.gridsize)
+        dummy_obs = self.get_vector_state()
         self.observation_space = spaces.Box(
-            low=0, high=255, shape=dummy_obs.shape, dtype=np.uint8
+            low=0, high=1, shape=dummy_obs.shape, dtype=float
         )
 
         self.last_obs = dummy_obs
@@ -182,16 +192,18 @@ class EnvGymWrapper(Env):
                 timedelta(seconds=self.global_step_time / self.in_between_steps)
             )
 
-        observation = self.get_env_img(self.gridsize)
+        # observation = self.get_env_img(self.gridsize)
+        observation = self.get_vector_state()
+
 
         reward = -1
         if (
-            self.env.order_manager.score > self.prev_score
-            and self.env.order_manager.score != 0
+            self.env.score > self.prev_score
+            and self.env.score != 0
         ):
-            self.prev_score = self.env.order_manager.score
+            self.prev_score = self.env.score
             reward = 100
-        elif self.env.order_manager.score < self.prev_score:
+        elif self.env.score < self.prev_score:
             self.prev_score = 0
             reward = -1
 
@@ -203,24 +215,27 @@ class EnvGymWrapper(Env):
         return observation, reward, terminated, truncated, info
 
     def reset(self, seed=None, options=None):
-        self.env: Environment = Environment(
-            env_config=environment_config_path,
-            layout_config=layout_path,
-            item_info=item_info_path,
-            as_files=True,
-        )
+        # self.env: Environment = Environment(
+        #     env_config=environment_config,
+        #     layout_config=layout,
+        #     item_info=item_info,
+        #     as_files=False
+        # )
+
+        self.env = deepcopy(vanilla_env)
 
         self.player_name = str(0)
         self.env.add_player(self.player_name)
         self.player_id = list(self.env.players.keys())[0]
-
         info = {}
-        return self.get_env_img(self.gridsize), info
+        # obs =  self.get_env_img(self.gridsize)
+        obs = self.get_vector_state()
+        return obs, info
 
     def render(self):
         observation = self.get_env_img(self.gridsize)
         img = observation.transpose((1, 2, 0))[:, :, ::-1]
-        print(img.shape)
+        # print(img.shape)
         img = cv2.resize(img, (img.shape[1] * 5, img.shape[0] * 5))
         cv2.imshow("Overcooked", img)
         cv2.waitKey(1)
@@ -239,6 +254,8 @@ class EnvGymWrapper(Env):
     def get_vector_state(self):
         grid, player, env_time, orders = self.env.get_vectorized_state("0")
 
+        obs = np.concatenate([grid.flatten(), player.flatten()], axis=0)
+        return obs
         # flatten: grid + player
         # concatenate all (env_time to array)
 
@@ -248,12 +265,13 @@ class EnvGymWrapper(Env):
         # return np.random.randint(len(self.action_space_map))
 
 
+
 def main():
     rl_agent_checkpoints = Path("./rl_agent_checkpoints")
     rl_agent_checkpoints.mkdir(exist_ok=True)
 
     config = {
-        "policy_type": "CnnPolicy",
+        "policy_type": "MlpPolicy",
         "total_timesteps": 1000000,  # hendric sagt eher so 300_000_000 schritte
         "env_id": "overcooked",
     }
@@ -265,7 +283,7 @@ def main():
     #     # save_code=True,  # optional
     # )
 
-    env = make_vec_env(EnvGymWrapper, n_envs=4)
+    env = make_vec_env(EnvGymWrapper, n_envs=64)
     # env = EnvGymWrapper()
     model_classes = [A2C, DQN, PPO]
 

overcooked_simulator/hooks.py

@@ -37,6 +37,9 @@ GAME_ENDED_STEP = "game_ended_step"
 
 PRE_STATE = "pre_state"
 
+PRE_STEP = "pre_step"
+POST_STEP = "post_step"
+
 STATE_DICT = "state_dict"
 
 JSON_STATE = "json_state"