Swimmer

public final class Swimmer : MuJoCoEnv

extension Swimmer: Env

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: viscous-friction coefficient While the default environment has n = 3, l_i = 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_i: angle of part i with respect to the x axis
• θ_i‘: 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(action²) 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)