Reacher#

This environment is part of the Mujoco environments which contains general information about the environment.


Action Space	`Box(-1.0, 1.0, (2,), float32)`
Observation Space	`Box(-inf, inf, (11,), float64)`
import	`gymnasium.make("Reacher-v4")`

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	sin(joint0)	hinge	unitless
3	sine of the angle of the second arm	-Inf	Inf	sin(joint1)	hinge	unitless
4	x-coordinate of the target	-Inf	Inf	target_x	slide	position (m)
5	y-coordinate 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 (constantly 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 End#

The episode ends when any of the following happens:

Truncation: The episode duration reaches a 50 timesteps (with a new random target popping up if the reacher’s fingertip reaches it before 50 timesteps)
Termination: 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)..

import gymnasium as gym
env = gym.make('Reacher-v4')

There is no v3 for Reacher, unlike the robot environments where a v3 and beyond take gymnasium.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale, etc.

Version History#

v4: All MuJoCo environments now use the MuJoCo bindings in mujoco >= 2.1.3
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)