Docs, copy-paste error [ci skip]

docs/01_tutorials/04_tictactoe.rst

@@ -127,29 +127,6 @@ Tianshou already provides some builtin classes for multi-agent learning. You can
 ::
 
     >>> from tianshou.data import Collector
-      >>> from tianshou.env import DummyVectorEnv
-      >>> from tianshou.policy import RandomPolicy, MultiAgentPolicyManager
-      >>>
-      >>> # agents should be wrapped into one policy,
-      >>> # which is responsible for calling the acting agent correctly
-      >>> # here we use two random agents
-      >>> policy = MultiAgentPolicyManager(
-      >>>     [MARLRandomPolicy(action_space=env.action_space), RandomPolicy(action_space=env.action_space)], env
-      >>> )
-      >>>
-      >>> # need to vectorize the environment for the collector
-      >>> env = DummyVectorEnv([lambda: env])
-      >>>
-      >>> # use collectors to collect a episode of trajectories
-      >>> # the reward is a vector, so we need a scalar metric to monitor the training
-      >>> collector = Collector(policy, env)
-      >>>
-      >>> # you will see a long trajectory showing the board status at each timestep
-      >>> result = collector.collect(n_episode=1, render=.1)
-      (only show the last 3 steps)
-           |     |
-        X  |  X  |  -
-
     >>> from tianshou.env import DummyVectorEnv
     >>> from tianshou.policy import RandomPolicy, MultiAgentPolicyManager
     >>>