Fix 166 and tests (#169)

* initial tests

* finish tests

* remove task during step

* fix tests

---------

Co-authored-by: rodrigodelazcano <rperezvicente@farama.org>

gymnasium_robotics/envs/franka_kitchen/kitchen_env.py

@@ -355,7 +355,8 @@ def compute_reward(
         desired_goal: "dict[str, np.ndarray]",
         info: "dict[str, Any]",
     ):
-        for task in info["tasks_to_complete"]:
+        self.step_task_completions.clear()
+        for task in self.tasks_to_complete:
             distance = np.linalg.norm(achieved_goal[task] - desired_goal[task])
             complete = distance < BONUS_THRESH
             if complete:
@@ -394,7 +395,6 @@ def _get_obs(self, robot_obs):
     def step(self, action):
         robot_obs, _, terminated, truncated, info = self.robot_env.step(action)
         obs = self._get_obs(robot_obs)
-        info = {"tasks_to_complete": list(self.tasks_to_complete)}
 
         reward = self.compute_reward(obs["achieved_goal"], self.goal, info)
 
@@ -405,13 +405,13 @@ def step(self, action):
                 for element in self.step_task_completions
             ]
 
+        info = {"tasks_to_complete": list(self.tasks_to_complete)}
         info["step_task_completions"] = self.step_task_completions.copy()
 
         for task in self.step_task_completions:
             if task not in self.episode_task_completions:
                 self.episode_task_completions.append(task)
         info["episode_task_completions"] = self.episode_task_completions
-        self.step_task_completions.clear()
         if self.terminate_on_tasks_completed:
             # terminate if there are no more tasks to complete
             terminated = len(self.episode_task_completions) == len(self.goal.keys())
@@ -423,9 +423,9 @@ def reset(self, *, seed: Optional[int] = None, **kwargs):
         self.episode_task_completions.clear()
         robot_obs, _ = self.robot_env.reset(seed=seed)
         obs = self._get_obs(robot_obs)
-        self.task_to_complete = set(self.goal.keys())
+        self.tasks_to_complete = set(self.goal.keys())
         info = {
-            "tasks_to_complete": self.task_to_complete,
+            "tasks_to_complete": list(self.tasks_to_complete),
             "episode_task_completions": [],
             "step_task_completions": [],
         }

tests/envs/franka_kitchen/test_kitchen_env.py

@@ -0,0 +1,123 @@
+from copy import deepcopy
+
+import gymnasium as gym
+import pytest
+
+from gymnasium_robotics.envs.franka_kitchen.kitchen_env import (
+    OBS_ELEMENT_GOALS,
+    OBS_ELEMENT_INDICES,
+)
+
+TASKS = ["microwave", "kettle"]
+
+
+@pytest.mark.parametrize(
+    "remove_task_when_completed, terminate_on_tasks_completed",
+    [[True, True], [False, False]],
+)
+def test_task_completion(remove_task_when_completed, terminate_on_tasks_completed):
+    """This test checks the different task completion configurations for the FrankaKitchen-v1 environment.
+
+    The test checks if the info items returned in each step (`tasks_to_complete`, `step_task_completions`, `episode_task_completions`) are correct and correspond
+    to the behavior of the environment configured at initialization with the arguments: `remove_task_when_completed` and `terminate_on_tasks_completed`.
+    """
+    env = gym.make(
+        "FrankaKitchen-v1",
+        tasks_to_complete=TASKS,
+        remove_task_when_completed=remove_task_when_completed,
+        terminate_on_tasks_completed=terminate_on_tasks_completed,
+    )
+    # Test task completion for 3 consecutive episodes
+    for _ in range(3):
+        tasks_to_complete = deepcopy(TASKS)
+        completed_tasks = set()
+        _, info = env.reset()
+        assert set(info["tasks_to_complete"]) == set(
+            TASKS
+        ), f"The item `tasks_to_complete` returned by info when the environment is reset: {set(info['tasks_to_complete'])}, must be equal to the `task_to_complete` argument used to initialize the environment: {tasks_to_complete}."
+        assert (
+            len(info["step_task_completions"]) == 0
+        ), f"The key `step_task_completions` returned by info when the environment is reset: {set(info['step_task_completions'])}, must be empty."
+        assert (
+            len(info["episode_task_completions"]) == 0
+        ), f"The key `episode_task_completions` returned by info when the environment is reset: {set(info['episode_task_completions'])}, must be empty."
+
+        terminated = False
+
+        # Complete a task sequentially for each environment step
+        for task in TASKS:
+            # Force task to be achieved
+            env.data.qpos[OBS_ELEMENT_INDICES[task]] = OBS_ELEMENT_GOALS[task]
+            _, _, terminated, _, info = env.step(env.action_space.sample())
+            completed_tasks.add(task)
+
+            assert (
+                set(info["episode_task_completions"]) == completed_tasks
+            ), f"The key `episode_task_completions` returned by info: {set(info['episode_task_completions'])}, must be equal to the tasks along the current episode: {completed_tasks}."
+            if remove_task_when_completed:
+                tasks_to_complete.remove(task)
+                assert set(info["tasks_to_complete"]) == set(
+                    tasks_to_complete
+                ), f"If environment is initialized with `remove_task_when_completed=True` the item `tasks_to_complete` returned by info: {set(info['tasks_to_complete'])}, must be equal to the tasks that haven't been completed yet: {tasks_to_complete}."
+                assert set(info["step_task_completions"]) == {
+                    task
+                }, f"The key `step_task_completions` returned by info: {set(info['step_task_completions'])}, must be equal to the tasks completed after the current step: {task}."
+
+            else:
+                assert set(info["tasks_to_complete"]) == set(
+                    tasks_to_complete
+                ), f"If environment is initialized with `remove_task_when_completed=False` the item `tasks_to_complete` returned by info: {set(info['tasks_to_complete'])}, must be equal to the set of tasks the environment was initialized with: {tasks_to_complete}."
+                assert (
+                    set(info["step_task_completions"]) == completed_tasks
+                ), f"The key `step_task_completions` returned by info: {set(info['step_task_completions'])}, must be equal to the tasks completed after the current step: {completed_tasks}."
+
+        if terminate_on_tasks_completed:
+            assert (
+                terminated
+            ), "If the environment is initialized with `terminate_on_tasks_complete=True`, the episode must terminate after all tasks are completed."
+        else:
+            assert (
+                not terminated
+            ), "If the environment is initialized with `terminate_on_tasks_complete=False`, the episode must not terminate after all tasks are completed."
+
+    # Complete a task during the same environment step
+    for _ in range(3):
+        tasks_to_complete = deepcopy(TASKS)
+        completed_tasks = set()
+        _, info = env.reset()
+
+        terminated = False
+
+        # Complete a task sequentially for each environment step
+        for task in TASKS:
+            # Force task to be achieved
+            env.data.qpos[OBS_ELEMENT_INDICES[task]] = OBS_ELEMENT_GOALS[task]
+            completed_tasks.add(task)
+
+        _, _, terminated, _, info = env.step(env.action_space.sample())
+        assert (
+            set(info["step_task_completions"]) == completed_tasks
+        ), f"The key `step_task_completions` returned by info: {set(info['step_task_completions'])}, must be equal to the tasks completed after the current step: {completed_tasks}."
+        assert (
+            set(info["episode_task_completions"]) == completed_tasks
+        ), f"The key `episode_task_completions` returned by info: {set(info['episode_task_completions'])}, must be equal to the tasks along the current episode: {completed_tasks}."
+        if remove_task_when_completed:
+            assert (
+                len(info["tasks_to_complete"]) == 0
+            ), f"If environment is initialized with `remove_task_when_completed=True` and all tasks were completed the item `tasks_to_complete` returned by info: {set(info['tasks_to_complete'])}, must be empty."
+
+        else:
+            assert set(info["tasks_to_complete"]) == set(
+                tasks_to_complete
+            ), f"If environment is initialized with `remove_task_when_completed=False` the item `tasks_to_complete` returned by info: {set(info['tasks_to_complete'])}, must be equal to the set of tasks the environment was initialized with: {tasks_to_complete}."
+
+        if terminate_on_tasks_completed:
+            assert (
+                terminated
+            ), "If the environment is initialized with `terminate_on_tasks_complete=True`, the episode must terminate after all tasks are completed."
+        else:
+            assert (
+                not terminated
+            ), "If the environment is initialized with `terminate_on_tasks_complete=False`, the episode must not terminate after all tasks are completed."
+
+    env.close()