[Fix] Edit the gym-unity Readme to fix some issue in the sample code (#5331)

* Edit the gym-unity Readme to fix some issue in the sample code

* Update gym-unity/README.md

Co-authored-by: andrewcoh <54679309+andrewcoh@users.noreply.github.com>

* addressing comments

* Adding the action_seed parameter to the documentation

Co-authored-by: andrewcoh <54679309+andrewcoh@users.noreply.github.com>

gym-unity/README.md

   Discrete. Otherwise, it will be converted into a MultiDiscrete. Defaults to
   `False`.
 
-- `allow_multiple_obs` will return a list of observations. The first elements contain the visual observations and the
-  last element contains the array of vector observations. If False the environment returns a single array (containing
-  a single visual observations, if present, otherwise the vector observation). Defaults to `False`.
+- `allow_multiple_obs` will return a list of observations. The first elements
+  contain the visual observations and the last element contains the array of
+  vector observations. If False the environment returns a single array (containing
+  a single visual observations, if present, otherwise the vector observation).
+  Defaults to `False`.
+
+- `action_space_seed` is the optional seed for action sampling. If non-None, will
+  be used to set the random seed on created gym.Space instances.
 
 The returned environment `env` will function as a gym.
 
 ```
 
 Next, create a file called `train_unity.py`. Then create an `/envs/` directory
-and build the GridWorld environment to that directory. For more information on
+and build the environment to that directory. For more information on
-[here](../docs/Learning-Environment-Executable.md). Add the following code to
-the `train_unity.py` file:
+[here](../docs/Learning-Environment-Executable.md). Note that because of
+limitations of the DQN baseline, the environment must have a single visual
+observation, a single discrete action and a single Agent in the scene.
+Add the following code to the `train_unity.py` file:
 
 ```python
 import gym
 from gym_unity.envs import UnityToGymWrapper
 
 def main():
-    unity_env = UnityEnvironment("./envs/GridWorld")
-    env = UnityToGymWrapper(unity_env, 0, uint8_visual=True)
+    unity_env = UnityEnvironment(<path-to-environment>)
+    env = UnityToGymWrapper(unity_env, uint8_visual=True)
-        "cnn", # conv_only is also a good choice for GridWorld
+        "cnn", # For visual inputs
         lr=2.5e-4,
         total_timesteps=1000000,
         buffer_size=50000,
     """
     def make_env(rank, use_visual=True): # pylint: disable=C0111
         def _thunk():
-            unity_env = UnityEnvironment(env_directory)
-            env = UnityToGymWrapper(unity_env, rank, uint8_visual=True)
+            unity_env = UnityEnvironment(env_directory, base_port=5000 + rank)
+            env = UnityToGymWrapper(unity_env, uint8_visual=True)
             env = Monitor(env, logger.get_dir() and os.path.join(logger.get_dir(), str(rank)))
             return env
         return _thunk
         return DummyVecEnv([make_env(rank, use_visual=False)])
 
 def main():
-    env = make_unity_env('./envs/GridWorld', 4, True)
+    env = make_unity_env(<path-to-environment>, 4, True)
     ppo2.learn(
         network="mlp",
         env=env,
 ```python
     game_version = 'v0' if sticky_actions else 'v4'
     full_game_name = '{}NoFrameskip-{}'.format(game_name, game_version)
-    unity_env = UnityEnvironment('./envs/GridWorld')
+    unity_env = UnityEnvironment(<path-to-environment>)
-`./envs/GridWorld` is the path to your built Unity executable. For more
+`<path-to-environment>` is the path to your built Unity executable. For more
 information on building Unity environments, see
 [here](../docs/Learning-Environment-Executable.md), and note the Limitations
 section below.
 
 Since Dopamine is designed around variants of DQN, it is only compatible with
 discrete action spaces, and specifically the Discrete Gym space. For
-environments that use branched discrete action spaces (e.g.
-[VisualBanana](../docs/Learning-Environment-Examples.md)), you can enable the
+environments that use branched discrete action spaces, you can enable the
 `flatten_branched` parameter in `UnityToGymWrapper`, which treats each
 combination of branched actions as separate actions.
 
 The hyperparameters provided by Dopamine are tailored to the Atari games, and
 you will likely need to adjust them for ML-Agents environments. Here is a sample
 `dopamine/agents/rainbow/configs/rainbow.gin` file that is known to work with
-GridWorld.
+a simple GridWorld.
 
 ```python
 import dopamine.agents.rainbow.rainbow_agent
 
 ![Dopamine on GridWorld](images/dopamine_gridworld_plot.png)
 
-### Example: VisualBanana
-
-As an example of using the `flatten_branched` option, we also used the Rainbow
-algorithm to train on the VisualBanana environment, and provide the results
-below. The same hyperparameters were used as in the GridWorld case, except that
-`replay_history` and `epsilon_decay` were increased to 100000.
-
-![Dopamine on VisualBanana](images/dopamine_visualbanana_plot.png)