Implement make_atari_env via AtariEnvFactory, eliminating duplication

examples/atari/atari_wrapper.py

@@ -9,7 +9,6 @@ import gymnasium as gym
 import numpy as np
 from gymnasium import Env
 
-from tianshou.env import ShmemVectorEnv
 from tianshou.highlevel.env import (
     EnvFactoryGymnasium,
     EnvMode,
@@ -324,68 +323,23 @@ def wrap_deepmind(
     return env
 
 
-def make_atari_env(task, seed, training_num, test_num, **kwargs):
+def make_atari_env(
+    task,
+    seed,
+    training_num,
+    test_num,
+    scale: int | bool = False,
+    frame_stack: int = 4,
+):
     """Wrapper function for Atari env.
 
     If EnvPool is installed, it will automatically switch to EnvPool's Atari env.
 
     :return: a tuple of (single env, training envs, test envs).
     """
-    if envpool is not None:
-        if kwargs.get("scale", 0):
-            warnings.warn(
-                "EnvPool does not include ScaledFloatFrame wrapper, "
-                "please set `x = x / 255.0` inside CNN network's forward function.",
-            )
-        # parameters convertion
-        train_envs = env = envpool.make_gymnasium(
-            task.replace("NoFrameskip-v4", "-v5"),
-            num_envs=training_num,
-            seed=seed,
-            episodic_life=True,
-            reward_clip=True,
-            stack_num=kwargs.get("frame_stack", 4),
-        )
-        test_envs = envpool.make_gymnasium(
-            task.replace("NoFrameskip-v4", "-v5"),
-            num_envs=test_num,
-            seed=seed,
-            episodic_life=False,
-            reward_clip=False,
-            stack_num=kwargs.get("frame_stack", 4),
-        )
-    else:
-        assert "NoFrameskip" in task
-        warnings.warn(
-            "Recommend using envpool (pip install envpool) to run Atari games more efficiently.",
-        )
-        env = wrap_deepmind(task, **kwargs)
-        train_envs = ShmemVectorEnv(
-            [
-                lambda: wrap_deepmind(
-                    gym.make(task),
-                    episode_life=True,
-                    clip_rewards=True,
-                    **kwargs,
-                )
-                for _ in range(training_num)
-            ],
-        )
-        test_envs = ShmemVectorEnv(
-            [
-                lambda: wrap_deepmind(
-                    gym.make(task),
-                    episode_life=False,
-                    clip_rewards=False,
-                    **kwargs,
-                )
-                for _ in range(test_num)
-            ],
-        )
-        env.seed(seed)
-        train_envs.seed(seed)
-        test_envs.seed(seed)
-    return env, train_envs, test_envs
+    env_factory = AtariEnvFactory(task, seed, frame_stack, scale=bool(scale))
+    envs = env_factory.create_envs(training_num, test_num)
+    return envs.env, envs.train_envs, envs.test_envs
 
 
 class AtariEnvFactory(EnvFactoryGymnasium):