chore: import upstream snapshot with attribution

doc/source/rllib/doc_code/custom_gym_env.py

@@ -0,0 +1,40 @@
+# __rllib-custom-gym-env-begin__
+import gymnasium as gym
+import numpy as np
+
+import ray
+from ray.rllib.algorithms.ppo import PPOConfig
+
+
+class SimpleCorridor(gym.Env):
+    def __init__(self, config):
+        self.end_pos = config["corridor_length"]
+        self.cur_pos = 0.0
+        self.action_space = gym.spaces.Discrete(2)  # right/left
+        self.observation_space = gym.spaces.Box(0.0, self.end_pos, shape=(1,))
+
+    def reset(self, *, seed=None, options=None):
+        self.cur_pos = 0.0
+        return np.array([self.cur_pos]), {}
+
+    def step(self, action):
+        if action == 0 and self.cur_pos > 0.0:  # move right (towards goal)
+            self.cur_pos -= 1.0
+        elif action == 1:  # move left (towards start)
+            self.cur_pos += 1.0
+        if self.cur_pos >= self.end_pos:
+            return np.array([0.0]), 1.0, True, True, {}
+        else:
+            return np.array([self.cur_pos]), -0.1, False, False, {}
+
+
+ray.init()
+
+config = PPOConfig().environment(SimpleCorridor, env_config={"corridor_length": 5})
+algo = config.build()
+
+for _ in range(3):
+    print(algo.train())
+
+algo.stop()
+# __rllib-custom-gym-env-end__