Walker2D

public final class Walker2D : MuJoCoEnv

extension Walker2D: Env

Description

This environment builds on the hopper environment based on the work done by Erez, Tassa, and Todorov in “Infinite Horizon Model Predictive Control for Nonlinear Periodic Tasks” by adding another set of legs making it possible for the robot to walker forward instead of hop. Like other Mujoco environments, this environment aims to increase the number of independent state and control variables as compared to the classic control environments. The walker is a two-dimensional two-legged figure that consist of four main body parts - a single torso at the top (with the two legs splitting after the torso), two thighs in the middle below the torso, two legs in the bottom below the thighs, and two feet attached to the legs on which the entire body rests. The goal is to make coordinate both sets of feet, legs, and thighs to move in the forward (right) direction by applying torques on the six hinges connecting the six body parts.

Action Space

The action space is a Box(-1, 1, (6,), float32). An action represents the torques applied at the hinge joints. | Num | Action | Control Min | Control Max | Name (in corresponding XML file) | Joint | Unit | |—–|—————————————-|————-|————-|———————————-|——-|————–| | 0 | Torque applied on the thigh rotor | -1 | 1 | thigh_joint | hinge | torque (N m) | | 1 | Torque applied on the leg rotor | -1 | 1 | leg_joint | hinge | torque (N m) | | 2 | Torque applied on the foot rotor | -1 | 1 | foot_joint | hinge | torque (N m) | | 3 | Torque applied on the left thigh rotor | -1 | 1 | thigh_left_joint | hinge | torque (N m) | | 4 | Torque applied on the left leg rotor | -1 | 1 | leg_left_joint | hinge | torque (N m) | | 5 | Torque applied on the left foot rotor | -1 | 1 | foot_left_joint | hinge | torque (N m) |

Observation Space

Observations consist of positional values of different body parts of the walker, followed by the velocities of those individual parts (their derivatives) with all the positions ordered before all the velocities. By default, observations do not include the x-coordinate of the top. It may be included by passing exclude_current_positions_from_observation=False during construction. In that case, the observation space will have 18 dimensions where the first dimension represent the x-coordinates of the top of the walker. Regardless of whether exclude_current_positions_from_observation was set to true or false, the x-coordinate of the top will be returned in info with key "x_position". By default, observation is a ndarray with shape (17,) where the elements correspond to the following: | Num | Observation | Min | Max | Name (in corresponding XML file) | Joint | Unit | | — | ———————————————— | —- | — | ——————————– | —– | ———————— | | 0 | z-coordinate of the top (height of hopper) | -Inf | Inf | rootz (torso) | slide | position (m) | | 1 | angle of the top | -Inf | Inf | rooty (torso) | hinge | angle (rad) | | 2 | angle of the thigh joint | -Inf | Inf | thigh_joint | hinge | angle (rad) | | 3 | angle of the leg joint | -Inf | Inf | leg_joint | hinge | angle (rad) | | 4 | angle of the foot joint | -Inf | Inf | foot_joint | hinge | angle (rad) | | 5 | angle of the left thigh joint | -Inf | Inf | thigh_left_joint | hinge | angle (rad) | | 6 | angle of the left leg joint | -Inf | Inf | leg_left_joint | hinge | angle (rad) | | 7 | angle of the left foot joint | -Inf | Inf | foot_left_joint | hinge | angle (rad) | | 8 | velocity of the x-coordinate of the top | -Inf | Inf | rootx | slide | velocity (m/s) | | 9 | velocity of the z-coordinate (height) of the top | -Inf | Inf | rootz | slide | velocity (m/s) | | 10 | angular velocity of the angle of the top | -Inf | Inf | rooty | hinge | angular velocity (rad/s) | | 11 | angular velocity of the thigh hinge | -Inf | Inf | thigh_joint | hinge | angular velocity (rad/s) | | 12 | angular velocity of the leg hinge | -Inf | Inf | leg_joint | hinge | angular velocity (rad/s) | | 13 | angular velocity of the foot hinge | -Inf | Inf | foot_joint | hinge | angular velocity (rad/s) | | 14 | angular velocity of the thigh hinge | -Inf | Inf | thigh_left_joint | hinge | angular velocity (rad/s) | | 15 | angular velocity of the leg hinge | -Inf | Inf | leg_left_joint | hinge | angular velocity (rad/s) | | 16 | angular velocity of the foot hinge | -Inf | Inf | foot_left_joint | hinge | angular velocity (rad/s) |

Rewards

The reward consists of three parts:

healthy_reward: Every timestep that the walker is alive, it receives a fixed reward of value healthy_reward,
forward_reward: A reward of walking 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 dependeent on the frame_skip parameter (default is 4), where the frametime is 0.002 - making the default dt = 4 * 0.002 = 0.008. This reward would be positive if the walker walks forward (right) desired.
ctrl_cost: A cost for penalising the walker if it takes actions that are too large. It is measured as ctrl_cost_weight * sum(action²) where ctrl_cost_weight is a parameter set for the control and has a default value of 0.001 The total reward returned is reward = healthy_reward bonus + forward_reward - ctrl_cost and info will also contain the individual reward terms ### Starting State All observations start in state (0.0, 1.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) with a uniform noise in the range of [-reset_noise_scale, reset_noise_scale] added to the values for stochasticity. ### Episode End The walker is said to be unhealthy if any of the following happens:
Any of the state space values is no longer finite
The height of the walker is not in the closed interval specified by healthy_z_range
The absolute value of the angle (observation[1] if exclude_current_positions_from_observation=False, else observation[2]) is not in the closed interval specified by healthy_angle_range If terminate_when_unhealthy=True is passed during construction (which is the default), the episode ends when any of the following happens:
Truncation: The episode duration reaches a 1000 timesteps
Termination: The walker is unhealthy If terminate_when_unhealthy=False is passed, the episode is ended only when 1000 timesteps are exceeded. ### Arguments No additional arguments are currently supported in v2 and lower. env = gym.make('Walker2d-v4') v3 and beyond take gym.make kwargs such as xml_file, ctrl_cost_weight, reset_noise_scale etc. env = gym.make('Walker2d-v4', ctrl_cost_weight=0.1, ....) | Parameter | Type | Default | Description | | ——————————————– | ——— | —————- | —————————————————————————————————————————————————————– | | xml_file | str | "walker2d.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-3 | Weight for ctr_cost term (see section on reward) | | healthy_reward | float | 1.0 | Constant reward given if the ant is “healthy” after timestep | | terminate_when_unhealthy | bool | True | If true, issue a done signal if the z-coordinate of the walker is no longer healthy | | healthy_z_range | tuple | (0.8, 2) | The z-coordinate of the top of the walker must be in this range to be considered healthy | | healthy_angle_range | tuple | (-1, 1) | The angle must be in this range to be considered healthy | | reset_noise_scale | float | 5e-3 | 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-coordinate 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)

model
Declaration
Swift

public let model: MjModel
data
Declaration
Swift

public var data: MjData


                    
                    
                    init(forwardRewardWeight:ctrlCostWeight:healthyReward:terminateWhenUnhealthy:healthyZRange:healthyAngleRange:resetNoiseScale:)

Declaration

Swift

public init(
  forwardRewardWeight: Double = 1.0, ctrlCostWeight: Double = 1e-3, healthyReward: Double = 1.0,
  terminateWhenUnhealthy: Bool = true, healthyZRange: ClosedRange<Double> = 0.8...2,
  healthyAngleRange: ClosedRange<Double> = -1...1, resetNoiseScale: Double = 5e-3
) throws


                    
                    
                    ActType

Declaration

Swift

public typealias ActType = Tensor<Float64>


                    
                    
                    ObsType

Declaration

Swift

public typealias ObsType = Tensor<Float64>

RewardType
Declaration
Swift

public typealias RewardType = Float
TerminatedType
Declaration
Swift

public typealias TerminatedType = Bool


                    
                    
                    step(action:)

Declaration

Swift

public func step(action: ActType) -> (ObsType, RewardType, TerminatedType, [String : Any])


                    
                    
                    reset(seed:)

Declaration

Swift

public func reset(seed: Int?) -> (ObsType, [String : Any])


                    
                    
                    rewardThreshold

Declaration

Swift

public static var rewardThreshold: Float { get }


                    
                    
                    actionSpace

Declaration

Swift

public static var actionSpace: [ClosedRange<Float>] { get }


                    
                    
                    stateSize

Declaration

Swift

public static var stateSize: Int { get }