Reacher#
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 |
(11,) |
Observation High |
[inf inf inf inf inf inf inf inf inf inf inf] |
Observation Low |
[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf] |
Import |
|
Description#
“Reacher” is a two-jointed robot arm. The goal is to move the robot’s end effector (called fingertip) close to a target that is spawned at a random position.
Action Space#
The action space is a Box(-1, 1, (2,), float32). An action (a, b) represents the torques applied at the hinge joints.
Num |
Action |
Control Min |
Control Max |
Name (in corresponding XML file) |
Joint |
Unit |
|---|---|---|---|---|---|---|
0 |
Torque applied at the first hinge (connecting the link to the point of fixture) |
-1 |
1 |
joint0 |
hinge |
torque (N m) |
1 |
Torque applied at the second hinge (connecting the two links) |
-1 |
1 |
joint1 |
hinge |
torque (N m) |
Observation Space#
Observations consist of
The cosine of the angles of the two arms
The sine of the angles of the two arms
The coordinates of the target
The angular velocities of the arms
The vector between the target and the reacher’s fingertip (3 dimensional with the last element being 0)
The observation is a ndarray with shape (11,) where the elements correspond to the following:
Num |
Observation |
Min |
Max |
Name (in corresponding XML file) |
Joint |
Unit |
|---|---|---|---|---|---|---|
0 |
cosine of the angle of the first arm |
-Inf |
Inf |
cos(joint0) |
hinge |
unitless |
1 |
cosine of the angle of the second arm |
-Inf |
Inf |
cos(joint1) |
hinge |
unitless |
2 |
sine of the angle of the first arm |
-Inf |
Inf |
cos(joint0) |
hinge |
unitless |
3 |
sine of the angle of the second arm |
-Inf |
Inf |
cos(joint1) |
hinge |
unitless |
4 |
x-coorddinate of the target |
-Inf |
Inf |
target_x |
slide |
position (m) |
5 |
y-coorddinate of the target |
-Inf |
Inf |
target_y |
slide |
position (m) |
6 |
angular velocity of the first arm |
-Inf |
Inf |
joint0 |
hinge |
angular velocity (rad/s) |
7 |
angular velocity of the second arm |
-Inf |
Inf |
joint1 |
hinge |
angular velocity (rad/s) |
8 |
x-value of position_fingertip - position_target |
-Inf |
Inf |
NA |
slide |
position (m) |
9 |
y-value of position_fingertip - position_target |
-Inf |
Inf |
NA |
slide |
position (m) |
10 |
z-value of position_fingertip - position_target (0 since reacher is 2d and z is same for both) |
-Inf |
Inf |
NA |
slide |
position (m) |
Most Gym environments just return the positions and velocity of the
joints in the .xml file as the state of the environment. However, in
reacher the state is created by combining only certain elements of the
position and velocity, and performing some function transformations on them.
If one is to read the .xml for reacher then they will find 4 joints:
Num |
Observation |
Min |
Max |
Name (in corresponding XML file) |
Joint |
Unit |
|---|---|---|---|---|---|---|
0 |
angle of the first arm |
-Inf |
Inf |
joint0 |
hinge |
angle (rad) |
1 |
angle of the second arm |
-Inf |
Inf |
joint1 |
hinge |
angle (rad) |
2 |
x-coordinate of the target |
-Inf |
Inf |
target_x |
slide |
position (m) |
3 |
y-coordinate of the target |
-Inf |
Inf |
target_y |
slide |
position (m) |
Rewards#
The reward consists of two parts:
reward_distance: This reward is a measure of how far the fingertip of the reacher (the unattached end) is from the target, with a more negative value assigned for when the reacher’s fingertip is further away from the target. It is calculated as the negative vector norm of (position of the fingertip - position of target), or -norm(“fingertip” - “target”).
reward_control: A negative reward for penalising the walker if it takes actions that are too large. It is measured as the negative squared Euclidean norm of the action, i.e. as - sum(action2).
The total reward returned is reward = reward_distance + reward_control
Unlike other environments, Reacher does not allow you to specify weights for the individual reward terms.
However, info does contain the keys reward_dist and reward_ctrl. Thus, if you’d like to weight the terms,
you should create a wrapper that computes the weighted reward from info.
Starting State#
All observations start in state (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) with a noise added for stochasticity. A uniform noise in the range [-0.1, 0.1] is added to the positional attributes, while the target position is selected uniformly at random in a disk of radius 0.2 around the origin. Independent, uniform noise in the range of [-0.005, 0.005] is added to the velocities, and the last element (“fingertip” - “target”) is calculated at the end once everything is set. The default setting has a framerate of 2 and a dt = 2 * 0.01 = 0.02
Episode Termination#
The episode terminates when any of the following happens:
The episode duration reaches a 50 timesteps (with a new random target popping up if the reacher’s fingertip reaches it before 50 timesteps)
Any of the state space values is no longer finite.
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)..
env = gym.make('Reacher-v2')
There is no v3 for Reacher, unlike the robot environments where a v3 and beyond take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc.
Version History#
v2: All continuous control environments now use mujoco_py >= 1.50
v1: max_time_steps raised to 1000 for robot based tasks (not including reacher, which has a max_time_steps of 50). Added reward_threshold to environments.
v0: Initial versions release (1.0.0)