Swimmer#
This environment is part of the Mujoco environments. Please read that page first for general information.
Action Space 
Box(1.0, 1.0, (2,), float32) 
Observation Shape 
(8,) 
Observation High 
[inf inf inf inf inf inf inf inf] 
Observation Low 
[inf inf inf inf inf inf inf inf] 
Import 

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”. 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_{i}: mass of part i (i ∈ {1…n})
l_{i}: length of part i (i ∈ {1…n})
k: viscousfriction coefficient
While the default environment has n = 3, l_{i} = 0.1, and k = 0.1. It is possible to tweak the MuJoCo XML files to increase the number of links, or to tweak any of the parameters.
Action Space#
The agent take a 2element vector for actions.
The action space is a continuous (action, action)
in [1, 1]
, where
action
represents the numerical 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 
rot2 
hinge 
torque (N m) 
1 
Torque applied on the second rotor 
1 
1 
rot3 
hinge 
torque (N m) 
Observation Space#
The state space consists of:
A_{0}: position of first point
θ_{i}: angle of part i with respect to the x axis
A_{0}, θ_{i}: their derivatives with respect to time (velocity and angular velocity)
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 
xcoordinate of the front tip 
Inf 
Inf 
slider1 
slide 
position (m) 
1 
ycoordinate of the front tip 
Inf 
Inf 
slider2 
slide 
position (m) 
2 
angle of the front tip 
Inf 
Inf 
rot 
hinge 
angle (rad) 
3 
angle of the second rotor 
Inf 
Inf 
rot2 
hinge 
angle (rad) 
4 
angle of the second rotor 
Inf 
Inf 
rot3 
hinge 
angle (rad) 
5 
velocity of the tip along the xaxis 
Inf 
Inf 
slider1 
slide 
velocity (m/s) 
6 
velocity of the tip along the yaxis 
Inf 
Inf 
slider2 
slide 
velocity (m/s) 
7 
angular velocity of front tip 
Inf 
Inf 
rot 
hinge 
angular velocity (rad/s) 
8 
angular velocity of second rotor 
Inf 
Inf 
rot2 
hinge 
angular velocity (rad/s) 
9 
angular velocity of third rotor 
Inf 
Inf 
rot3 
hinge 
angular velocity (rad/s) 
Note:
In practice (and Gym implementation), the first two positional elements are
omitted from the state space since the reward function is calculated based
on those values. Therefore, observation space has shape (8,)
and looks like:
Num 
Observation 
Min 
Max 
Name (in corresponding XML file) 
Joint 
Unit 

0 
angle of the front tip 
Inf 
Inf 
rot 
hinge 
angle (rad) 
1 
angle of the second rotor 
Inf 
Inf 
rot2 
hinge 
angle (rad) 
2 
angle of the second rotor 
Inf 
Inf 
rot3 
hinge 
angle (rad) 
3 
velocity of the tip along the xaxis 
Inf 
Inf 
slider1 
slide 
velocity (m/s) 
4 
velocity of the tip along the yaxis 
Inf 
Inf 
slider2 
slide 
velocity (m/s) 
5 
angular velocity of front tip 
Inf 
Inf 
rot 
hinge 
angular velocity (rad/s) 
6 
angular velocity of second rotor 
Inf 
Inf 
rot2 
hinge 
angular velocity (rad/s) 
7 
angular velocity of third rotor 
Inf 
Inf 
rot3 
hinge 
angular velocity (rad/s) 
Rewards#
The reward consists of two parts:
reward_front: A reward of moving forward which is measured as (xcoordinate before action  xcoordinate after action)/dt. dt is the time between actions and is dependent on the frame_skip parameter (default is 4), where the dt for one frame is 0.01  making the default dt = 4 * 0.01 = 0.04. This reward would be positive if the swimmer swims right as desired.
reward_control: A negative reward for penalising the swimmer if it takes actions that are too large. It is measured as coefficient x sum(action^{2}) where coefficient is a parameter set for the control and has a default value of 0.0001
The total reward returned is reward = reward_front + reward_control
Starting State#
All observations start in state (0,0,0,0,0,0,0,0) with a Uniform noise in the range of [0.1, 0.1] is added to the initial state for stochasticity.
Episode Termination#
The episode terminates when the episode length is greater than 1000.
Arguments#
No additional arguments are currently supported (in v2 and lower), but modifications can be made to the XML file in the assets folder (or by changing the path to a modified XML file in another folder).
gym.make('Swimmerv2')
v3 and v4 take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc.
env = gym.make('Swimmerv4', ctrl_cost_weight=0.1, ....)
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)