We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[rllib] [docs] Document multi-agent support
We should document the new multi-agent support in rllib and have some examples in readthedocs. It would be good to cover the supported cases and which ones are not yet supported (or provide workarounds).

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `python/ray/rllib/examples/multiagent_pendulum_env.py`
Content:
```
1 from gym.spaces import Box, Tuple
2 from gym.utils import seeding
3 from gym.envs.classic_control.pendulum import PendulumEnv
4 import numpy as np
5 
6 """
7  Multiagent pendulum that sums its torques to generate an action
8 """
9 
10 
11 class MultiAgentPendulumEnv(PendulumEnv):
12     metadata = {
13       'render.modes': ['human', 'rgb_array'],
14       'video.frames_per_second': 30
15     }
16 
17     def __init__(self):
18         self.max_speed = 8
19         self.max_torque = 2.
20         self.dt = .05
21         self.viewer = None
22 
23         high = np.array([1., 1., self.max_speed])
24         self.action_space = [Box(low=-self.max_torque / 2,
25                                  high=self.max_torque / 2, shape=(1,))
26                              for _ in range(2)]
27         self.observation_space = Tuple(tuple(Box(low=-high, high=high)
28                                              for _ in range(2)))
29 
30         self._seed()
31 
32     def _seed(self, seed=None):
33         self.np_random, seed = seeding.np_random(seed)
34         return [seed]
35 
36     def _step(self, u):
37         th, thdot = self.state  # th := theta
38 
39         summed_u = np.sum(u)
40         g = 10.
41         m = 1.
42         length = 1.
43         dt = self.dt
44 
45         summed_u = np.clip(summed_u, -self.max_torque, self.max_torque)
46         self.last_u = summed_u  # for rendering
47         costs = self.angle_normalize(th) ** 2 + .1 * thdot ** 2 + \
48             .001 * (summed_u ** 2)
49 
50         newthdot = thdot + (-3 * g / (2 * length) * np.sin(th + np.pi) +
51                             3. / (m * length ** 2) * summed_u) * dt
52         newth = th + newthdot * dt
53         newthdot = np.clip(newthdot, -self.max_speed, self.max_speed)
54 
55         self.state = np.array([newth, newthdot])
56         return self._get_obs(), -costs, False, {}
57 
58     def _reset(self):
59         high = np.array([np.pi, 1])
60         self.state = self.np_random.uniform(low=-high, high=high)
61         self.last_u = None
62         return self._get_obs()
63 
64     def _get_obs(self):
65         theta, thetadot = self.state
66         return [np.array([np.cos(theta), np.sin(theta), thetadot])
67                 for _ in range(2)]
68 
69     def angle_normalize(self, x):
70         return (((x + np.pi) % (2 * np.pi)) - np.pi)
71
```

Path: `python/ray/rllib/examples/multiagent_mountaincar_env.py`
Content:
```
1 import math
2 from gym.spaces import Box, Tuple, Discrete
3 import numpy as np
4 from gym.envs.classic_control.mountain_car import MountainCarEnv
5 
6 """
7 Multiagent mountain car that sums and then
8 averages its actions to produce the velocity
9 """
10 
11 
12 class MultiAgentMountainCarEnv(MountainCarEnv):
13     def __init__(self):
14         self.min_position = -1.2
15         self.max_position = 0.6
16         self.max_speed = 0.07
17         self.goal_position = 0.5
18 
19         self.low = np.array([self.min_position, -self.max_speed])
20         self.high = np.array([self.max_position, self.max_speed])
21 
22         self.viewer = None
23 
24         self.action_space = [Discrete(3) for _ in range(2)]
25         self.observation_space = Tuple(tuple(Box(self.low, self.high)
26                                              for _ in range(2)))
27 
28         self._seed()
29         self.reset()
30 
31     def _step(self, action):
32         summed_act = 0.5 * np.sum(action)
33 
34         position, velocity = self.state
35         velocity += (summed_act - 1) * 0.001
36         velocity += math.cos(3 * position) * (-0.0025)
37         velocity = np.clip(velocity, -self.max_speed, self.max_speed)
38         position += velocity
39         position = np.clip(position, self.min_position, self.max_position)
40         if (position == self.min_position and velocity < 0):
41             velocity = 0
42 
43         done = bool(position >= self.goal_position)
44 
45         reward = position
46 
47         self.state = (position, velocity)
48         return [np.array(self.state) for _ in range(2)], reward, done, {}
49 
50     def _reset(self):
51         self.state = np.array([self.np_random.uniform(low=-0.6, high=-0.4), 0])
52         return [np.array(self.state) for _ in range(2)]
53
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/python/ray/rllib/examples/multiagent_mountaincar_env.py b/python/ray/rllib/examples/multiagent_mountaincar_env.py
--- a/python/ray/rllib/examples/multiagent_mountaincar_env.py
+++ b/python/ray/rllib/examples/multiagent_mountaincar_env.py
@@ -22,8 +22,8 @@
         self.viewer = None
 
         self.action_space = [Discrete(3) for _ in range(2)]
-        self.observation_space = Tuple(tuple(Box(self.low, self.high)
-                                             for _ in range(2)))
+        self.observation_space = Tuple([
+            Box(self.low, self.high) for _ in range(2)])
 
         self._seed()
         self.reset()
diff --git a/python/ray/rllib/examples/multiagent_pendulum_env.py b/python/ray/rllib/examples/multiagent_pendulum_env.py
--- a/python/ray/rllib/examples/multiagent_pendulum_env.py
+++ b/python/ray/rllib/examples/multiagent_pendulum_env.py
@@ -24,8 +24,8 @@
         self.action_space = [Box(low=-self.max_torque / 2,
                                  high=self.max_torque / 2, shape=(1,))
                              for _ in range(2)]
-        self.observation_space = Tuple(tuple(Box(low=-high, high=high)
-                                             for _ in range(2)))
+        self.observation_space = Tuple([
+            Box(low=-high, high=high) for _ in range(2)])
 
         self._seed()

{"golden_diff": "diff --git a/python/ray/rllib/examples/multiagent_mountaincar_env.py b/python/ray/rllib/examples/multiagent_mountaincar_env.py\n--- a/python/ray/rllib/examples/multiagent_mountaincar_env.py\n+++ b/python/ray/rllib/examples/multiagent_mountaincar_env.py\n@@ -22,8 +22,8 @@\n         self.viewer = None\n \n         self.action_space = [Discrete(3) for _ in range(2)]\n-        self.observation_space = Tuple(tuple(Box(self.low, self.high)\n-                                             for _ in range(2)))\n+        self.observation_space = Tuple([\n+            Box(self.low, self.high) for _ in range(2)])\n \n         self._seed()\n         self.reset()\ndiff --git a/python/ray/rllib/examples/multiagent_pendulum_env.py b/python/ray/rllib/examples/multiagent_pendulum_env.py\n--- a/python/ray/rllib/examples/multiagent_pendulum_env.py\n+++ b/python/ray/rllib/examples/multiagent_pendulum_env.py\n@@ -24,8 +24,8 @@\n         self.action_space = [Box(low=-self.max_torque / 2,\n                                  high=self.max_torque / 2, shape=(1,))\n                              for _ in range(2)]\n-        self.observation_space = Tuple(tuple(Box(low=-high, high=high)\n-                                             for _ in range(2)))\n+        self.observation_space = Tuple([\n+            Box(low=-high, high=high) for _ in range(2)])\n \n         self._seed()\n", "issue": "[rllib] [docs] Document multi-agent support\nWe should document the new multi-agent support in rllib and have some examples in readthedocs. It would be good to cover the supported cases and which ones are not yet supported (or provide workarounds).\n", "before_files": [{"content": "from gym.spaces import Box, Tuple\nfrom gym.utils import seeding\nfrom gym.envs.classic_control.pendulum import PendulumEnv\nimport numpy as np\n\n\"\"\"\n Multiagent pendulum that sums its torques to generate an action\n\"\"\"\n\n\nclass MultiAgentPendulumEnv(PendulumEnv):\n    metadata = {\n      'render.modes': ['human', 'rgb_array'],\n      'video.frames_per_second': 30\n    }\n\n    def __init__(self):\n        self.max_speed = 8\n        self.max_torque = 2.\n        self.dt = .05\n        self.viewer = None\n\n        high = np.array([1., 1., self.max_speed])\n        self.action_space = [Box(low=-self.max_torque / 2,\n                                 high=self.max_torque / 2, shape=(1,))\n                             for _ in range(2)]\n        self.observation_space = Tuple(tuple(Box(low=-high, high=high)\n                                             for _ in range(2)))\n\n        self._seed()\n\n    def _seed(self, seed=None):\n        self.np_random, seed = seeding.np_random(seed)\n        return [seed]\n\n    def _step(self, u):\n        th, thdot = self.state  # th := theta\n\n        summed_u = np.sum(u)\n        g = 10.\n        m = 1.\n        length = 1.\n        dt = self.dt\n\n        summed_u = np.clip(summed_u, -self.max_torque, self.max_torque)\n        self.last_u = summed_u  # for rendering\n        costs = self.angle_normalize(th) ** 2 + .1 * thdot ** 2 + \\\n            .001 * (summed_u ** 2)\n\n        newthdot = thdot + (-3 * g / (2 * length) * np.sin(th + np.pi) +\n                            3. / (m * length ** 2) * summed_u) * dt\n        newth = th + newthdot * dt\n        newthdot = np.clip(newthdot, -self.max_speed, self.max_speed)\n\n        self.state = np.array([newth, newthdot])\n        return self._get_obs(), -costs, False, {}\n\n    def _reset(self):\n        high = np.array([np.pi, 1])\n        self.state = self.np_random.uniform(low=-high, high=high)\n        self.last_u = None\n        return self._get_obs()\n\n    def _get_obs(self):\n        theta, thetadot = self.state\n        return [np.array([np.cos(theta), np.sin(theta), thetadot])\n                for _ in range(2)]\n\n    def angle_normalize(self, x):\n        return (((x + np.pi) % (2 * np.pi)) - np.pi)\n", "path": "python/ray/rllib/examples/multiagent_pendulum_env.py"}, {"content": "import math\nfrom gym.spaces import Box, Tuple, Discrete\nimport numpy as np\nfrom gym.envs.classic_control.mountain_car import MountainCarEnv\n\n\"\"\"\nMultiagent mountain car that sums and then\naverages its actions to produce the velocity\n\"\"\"\n\n\nclass MultiAgentMountainCarEnv(MountainCarEnv):\n    def __init__(self):\n        self.min_position = -1.2\n        self.max_position = 0.6\n        self.max_speed = 0.07\n        self.goal_position = 0.5\n\n        self.low = np.array([self.min_position, -self.max_speed])\n        self.high = np.array([self.max_position, self.max_speed])\n\n        self.viewer = None\n\n        self.action_space = [Discrete(3) for _ in range(2)]\n        self.observation_space = Tuple(tuple(Box(self.low, self.high)\n                                             for _ in range(2)))\n\n        self._seed()\n        self.reset()\n\n    def _step(self, action):\n        summed_act = 0.5 * np.sum(action)\n\n        position, velocity = self.state\n        velocity += (summed_act - 1) * 0.001\n        velocity += math.cos(3 * position) * (-0.0025)\n        velocity = np.clip(velocity, -self.max_speed, self.max_speed)\n        position += velocity\n        position = np.clip(position, self.min_position, self.max_position)\n        if (position == self.min_position and velocity < 0):\n            velocity = 0\n\n        done = bool(position >= self.goal_position)\n\n        reward = position\n\n        self.state = (position, velocity)\n        return [np.array(self.state) for _ in range(2)], reward, done, {}\n\n    def _reset(self):\n        self.state = np.array([self.np_random.uniform(low=-0.6, high=-0.4), 0])\n        return [np.array(self.state) for _ in range(2)]\n", "path": "python/ray/rllib/examples/multiagent_mountaincar_env.py"}], "after_files": [{"content": "from gym.spaces import Box, Tuple\nfrom gym.utils import seeding\nfrom gym.envs.classic_control.pendulum import PendulumEnv\nimport numpy as np\n\n\"\"\"\n Multiagent pendulum that sums its torques to generate an action\n\"\"\"\n\n\nclass MultiAgentPendulumEnv(PendulumEnv):\n    metadata = {\n      'render.modes': ['human', 'rgb_array'],\n      'video.frames_per_second': 30\n    }\n\n    def __init__(self):\n        self.max_speed = 8\n        self.max_torque = 2.\n        self.dt = .05\n        self.viewer = None\n\n        high = np.array([1., 1., self.max_speed])\n        self.action_space = [Box(low=-self.max_torque / 2,\n                                 high=self.max_torque / 2, shape=(1,))\n                             for _ in range(2)]\n        self.observation_space = Tuple([\n            Box(low=-high, high=high) for _ in range(2)])\n\n        self._seed()\n\n    def _seed(self, seed=None):\n        self.np_random, seed = seeding.np_random(seed)\n        return [seed]\n\n    def _step(self, u):\n        th, thdot = self.state  # th := theta\n\n        summed_u = np.sum(u)\n        g = 10.\n        m = 1.\n        length = 1.\n        dt = self.dt\n\n        summed_u = np.clip(summed_u, -self.max_torque, self.max_torque)\n        self.last_u = summed_u  # for rendering\n        costs = self.angle_normalize(th) ** 2 + .1 * thdot ** 2 + \\\n            .001 * (summed_u ** 2)\n\n        newthdot = thdot + (-3 * g / (2 * length) * np.sin(th + np.pi) +\n                            3. / (m * length ** 2) * summed_u) * dt\n        newth = th + newthdot * dt\n        newthdot = np.clip(newthdot, -self.max_speed, self.max_speed)\n\n        self.state = np.array([newth, newthdot])\n        return self._get_obs(), -costs, False, {}\n\n    def _reset(self):\n        high = np.array([np.pi, 1])\n        self.state = self.np_random.uniform(low=-high, high=high)\n        self.last_u = None\n        return self._get_obs()\n\n    def _get_obs(self):\n        theta, thetadot = self.state\n        return [np.array([np.cos(theta), np.sin(theta), thetadot])\n                for _ in range(2)]\n\n    def angle_normalize(self, x):\n        return (((x + np.pi) % (2 * np.pi)) - np.pi)\n", "path": "python/ray/rllib/examples/multiagent_pendulum_env.py"}, {"content": "import math\nfrom gym.spaces import Box, Tuple, Discrete\nimport numpy as np\nfrom gym.envs.classic_control.mountain_car import MountainCarEnv\n\n\"\"\"\nMultiagent mountain car that sums and then\naverages its actions to produce the velocity\n\"\"\"\n\n\nclass MultiAgentMountainCarEnv(MountainCarEnv):\n    def __init__(self):\n        self.min_position = -1.2\n        self.max_position = 0.6\n        self.max_speed = 0.07\n        self.goal_position = 0.5\n\n        self.low = np.array([self.min_position, -self.max_speed])\n        self.high = np.array([self.max_position, self.max_speed])\n\n        self.viewer = None\n\n        self.action_space = [Discrete(3) for _ in range(2)]\n        self.observation_space = Tuple([\n            Box(self.low, self.high) for _ in range(2)])\n\n        self._seed()\n        self.reset()\n\n    def _step(self, action):\n        summed_act = 0.5 * np.sum(action)\n\n        position, velocity = self.state\n        velocity += (summed_act - 1) * 0.001\n        velocity += math.cos(3 * position) * (-0.0025)\n        velocity = np.clip(velocity, -self.max_speed, self.max_speed)\n        position += velocity\n        position = np.clip(position, self.min_position, self.max_position)\n        if (position == self.min_position and velocity < 0):\n            velocity = 0\n\n        done = bool(position >= self.goal_position)\n\n        reward = position\n\n        self.state = (position, velocity)\n        return [np.array(self.state) for _ in range(2)], reward, done, {}\n\n    def _reset(self):\n        self.state = np.array([self.np_random.uniform(low=-0.6, high=-0.4), 0])\n        return [np.array(self.state) for _ in range(2)]\n", "path": "python/ray/rllib/examples/multiagent_mountaincar_env.py"}]}