[RLlib] MAML: Add cartpole mass test for PyTorch. (ray-project#13679)

python/requirements_rllib.txt

@@ -13,3 +13,6 @@ pettingzoo>=1.4.0
 # For tests on RecSim and Kaggle envs.
 recsim
 kaggle_environments
+
+# For MAML on PyTorch.
+higher

rllib/agents/maml/tests/test_maml.py

@@ -23,15 +23,21 @@ def test_maml_compilation(self):
         num_iterations = 1
 
         # Test for tf framework (torch not implemented yet).
-        for _ in framework_iterator(config, frameworks=("tf")):
-            trainer = maml.MAMLTrainer(
-                config=config,
-                env="ray.rllib.examples.env.pendulum_mass.PendulumMassEnv")
-            for i in range(num_iterations):
-                trainer.train()
-            check_compute_single_action(
-                trainer, include_prev_action_reward=True)
-            trainer.stop()
+        for fw in framework_iterator(config, frameworks=("tf", "torch")):
+            for env in [
+                    "pendulum_mass.PendulumMassEnv",
+                    "cartpole_mass.CartPoleMassEnv"
+            ]:
+                if fw == "tf" and env.startswith("cartpole"):
+                    continue
+                print("env={}".format(env))
+                env_ = "ray.rllib.examples.env.{}".format(env)
+                trainer = maml.MAMLTrainer(config=config, env=env_)
+                for i in range(num_iterations):
+                    trainer.train()
+                check_compute_single_action(
+                    trainer, include_prev_action_reward=True)
+                trainer.stop()
 
 
 if __name__ == "__main__":

rllib/examples/env/cartpole_mass.py

@@ -0,0 +1,31 @@
+import numpy as np
+import gym
+from gym.envs.classic_control.cartpole import CartPoleEnv
+from ray.rllib.env.meta_env import MetaEnv
+
+
+class CartPoleMassEnv(CartPoleEnv, gym.utils.EzPickle, MetaEnv):
+    """CartPoleMassEnv varies the weights of the cart and the pole.
+    """
+
+    def sample_tasks(self, n_tasks):
+        # Sample new cart- and pole masses (random floats between 0.5 and 2.0
+        # (cart) and between 0.05 and 0.2 (pole)).
+        cart_masses = np.random.uniform(low=0.5, high=2.0, size=(n_tasks, 1))
+        pole_masses = np.random.uniform(low=0.05, high=0.2, size=(n_tasks, 1))
+        return np.concatenate([cart_masses, pole_masses], axis=-1)
+
+    def set_task(self, task):
+        """
+        Args:
+            task (Tuple[float]): Masses of the cart and the pole.
+        """
+        self.masscart = task[0]
+        self.masspole = task[1]
+
+    def get_task(self):
+        """
+        Returns:
+            Tuple[float]: The current mass of the cart- and pole.
+        """
+        return np.array([self.masscart, self.masspole])

rllib/examples/env/pendulum_mass.py

@@ -11,19 +11,22 @@ class PendulumMassEnv(PendulumEnv, gym.utils.EzPickle, MetaEnv):
     """
 
     def sample_tasks(self, n_tasks):
-        # Mass is a random float between 0.5 and 2
+        # Sample new pendulum masses (random floats between 0.5 and 2).
         return np.random.uniform(low=0.5, high=2.0, size=(n_tasks, ))
 
     def set_task(self, task):
         """
         Args:
-            task: task of the meta-learning environment
+            task (float): Task of the meta-learning environment (here: mass of
+                the pendulum).
         """
+        # self.m is the mass property of the pendulum.
         self.m = task
 
     def get_task(self):
         """
         Returns:
-            task: task of the meta-learning environment
+            float: The current mass of the pendulum (self.m in the PendulumEnv
+                object).
         """
         return self.m