class_docstr: ### Description
  
  This environment corresponds to the Swimmer environment described in Rémi Coulom's PhD thesis
  ["Reinforcement Learning Using Neural Networks, with Applications to Motor Control"](https://tel.archives-ouvertes.fr/tel-00003985/document).
  The environment aims to increase the number of independent state and control
  variables as compared to the classic control environments. The swimmers
  consist of three or more segments ('***links***') and one less articulation
  joints ('***rotors***') - one rotor joint connecting exactly two links to
  form a linear chain. The swimmer is suspended in a two dimensional pool and
  always starts in the same position (subject to some deviation drawn from an
  uniform distribution), and the goal is to move as fast as possible towards
  the right by applying torque on the rotors and using the fluids friction.
  
  ### Notes
  
  The problem parameters are:
  Problem parameters:
  * *n*: number of body parts
  * *m<sub>i</sub>*: mass of part *i* (*i* ∈ {1...n})
  * *l<sub>i</sub>*: length of part *i* (*i* ∈ {1...n})
  * *k*: viscous-friction coefficient
  
  While the default environment has *n* = 3, *l<sub>i</sub>* = 0.1,
  and *k* = 0.1. It is possible to pass a custom MuJoCo XML file during construction to increase the
  number of links, or to tweak any of the parameters.
  
  ### Action Space
  The action space is a `Box(-1, 1, (2,), float32)`. An action represents the torques applied between *links*
  
  | Num | Action                             | Control Min | Control Max | Name (in corresponding XML file) | Joint | Unit         |
  |-----|------------------------------------|-------------|-------------|----------------------------------|-------|--------------|
  | 0   | Torque applied on the first rotor  | -1          | 1           | motor1_rot                       | hinge | torque (N m) |
  | 1   | Torque applied on the second rotor | -1          | 1           | motor2_rot                       | hinge | torque (N m) |
  
  ### Observation Space
  
  By default, observations consists of:
  * θ<sub>i</sub>: angle of part *i* with respect to the *x* axis
  * θ<sub>i</sub>': its derivative with respect to time (angular velocity)
  
  In the default case, observations do not include the x- and y-coordinates of the front tip. These may
  be included by passing `exclude_current_positions_from_observation=False` during construction.
  Then, the observation space will have 10 dimensions where the first two dimensions
  represent the x- and y-coordinates of the front tip.
  Regardless of whether `exclude_current_positions_from_observation` was set to true or false, the x- and y-coordinates
  will be returned in `info` with keys `"x_position"` and `"y_position"`, respectively.
  
  By default, the observation is a `ndarray` with shape `(8,)` where the elements correspond to the following:
  
  | Num | Observation                          | Min  | Max | Name (in corresponding XML file) | Joint | Unit                     |
  | --- | ------------------------------------ | ---- | --- | -------------------------------- | ----- | ------------------------ |
  | 0   | angle of the front tip               | -Inf | Inf | free_body_rot                    | hinge | angle (rad)              |
  | 1   | angle of the first rotor             | -Inf | Inf | motor1_rot                       | hinge | angle (rad)              |
  | 2   | angle of the second rotor            | -Inf | Inf | motor2_rot                       | hinge | angle (rad)              |
  | 3   | velocity of the tip along the x-axis | -Inf | Inf | slider1                          | slide | velocity (m/s)           |
  | 4   | velocity of the tip along the y-axis | -Inf | Inf | slider2                          | slide | velocity (m/s)           |
  | 5   | angular velocity of front tip        | -Inf | Inf | free_body_rot                    | hinge | angular velocity (rad/s) |
  | 6   | angular velocity of first rotor      | -Inf | Inf | motor1_rot                       | hinge | angular velocity (rad/s) |
  | 7   | angular velocity of second rotor     | -Inf | Inf | motor2_rot                             | hinge | angular velocity (rad/s) |
  
  ### Rewards
  The reward consists of two parts:
  - *forward_reward*: A reward of moving forward which is measured
  as *`forward_reward_weight` * (x-coordinate before action - x-coordinate after action)/dt*. *dt* is
  the time between actions and is dependent on the frame_skip parameter
  (default is 4), where the frametime is 0.01 - making the
  default *dt = 4 * 0.01 = 0.04*. This reward would be positive if the swimmer
  swims right as desired.
  - *ctrl_cost*: A cost for penalising the swimmer if it takes
  actions that are too large. It is measured as *`ctrl_cost_weight` *
  sum(action<sup>2</sup>)* where *`ctrl_cost_weight`* is a parameter set for the
  control and has a default value of 1e-4
  
  The total reward returned is ***reward*** *=* *forward_reward - ctrl_cost* and `info` will also contain the individual reward terms
  
  ### Starting State
  All observations start in state (0,0,0,0,0,0,0,0) with a Uniform noise in the range of [-`reset_noise_scale`, `reset_noise_scale`] is added to the initial state for stochasticity.
  
  ### Episode End
  The episode truncates when the episode length is greater than 1000.
  
  ### Arguments
  
  No additional arguments are currently supported in v2 and lower.
  
  ```
  gym.make('Swimmer-v4')
  ```
  
  v3 and v4 take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc.
  
  ```
  env = gym.make('Swimmer-v4', ctrl_cost_weight=0.1, ....)
  ```
  
  | Parameter                                    | Type      | Default         | Description                                                                                                                                                               |
  | -------------------------------------------- | --------- | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
  | `xml_file`                                   | **str**   | `"swimmer.xml"` | Path to a MuJoCo model                                                                                                                                                    |
  | `forward_reward_weight`                      | **float** | `1.0`           | Weight for _forward_reward_ term (see section on reward)                                                                                                                  |
  | `ctrl_cost_weight`                           | **float** | `1e-4`          | Weight for _ctrl_cost_ term (see section on reward)                                                                                                                       |
  | `reset_noise_scale`                          | **float** | `0.1`           | Scale of random perturbations of initial position and velocity (see section on Starting State)                                                                            |
  | `exclude_current_positions_from_observation` | **bool**  | `True`          | Whether or not to omit the x- and y-coordinates from observations. Excluding the position can serve as an inductive bias to induce position-agnostic behavior in policies |
  
  
  ### Version History
  
  * v4: all mujoco environments now use the mujoco bindings in mujoco>=2.1.3
  * v3: support for gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc. rgb rendering comes from tracking camera (so agent does not run away from screen)
  * v2: All continuous control environments now use mujoco_py >= 1.50
  * v1: max_time_steps raised to 1000 for robot based tasks. Added reward_threshold to environments.
  * v0: Initial versions release (1.0.0)
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code: def viewer_setup(self):
      assert self.viewer is not None
      for key, value in DEFAULT_CAMERA_CONFIG.items():
          if isinstance(value, np.ndarray):
              getattr(self.viewer.cam, key)[:] = value
          else:
              setattr(self.viewer.cam, key, value)
  
file_path: gym/gym/envs/mujoco/swimmer_v4.py
imports: import numpy
  from gym import utils
  from gym.envs.mujoco import MujocoEnv
  from gym.spaces import Box
label_desc: Use image node_id 6 for calling the SwimmerEnv obj's underlying member method code with example usage: obj.viewer_setup() without return types