Custom gym environment: Mass-spring-damper-system

TODO: Visualize spring and damper

OBS: Baselines/spinningups PPO/TRPO/ACKTR does not take action limits into account. So must either change agent (clipping action), or the environmnet(allow all forces) for it to work.

A custom made gym environment for the classic, super simple control problem of a mass spring damper system.

1. Add MassSpringDamper_env.py to .../gym/envs/classic_control, ... being wherever your gym installation is.

2. Add the following line

from gym.envs.classic_control.MassSpringDamper_env import MassSpringDamperEnv

to gym/envs/classic_control/__init__.py

3. Add the following lines

register(
    id='MassSpringDamper-v0',
    entry_point='gym.envs.classic_control:MassSpringDamperEnv',
)

to gym/envs/__init__.py

4. Run it like a normal gym environment! For example:

import gym
import gym.envs

env = gym.make('MassSpringDamper-v0')

env.reset(goal_x, goal_x_dot) # if no goal is stated it is set to 0,0
for _ in range(1000):
    env.render()
    action = env.action_space.sample()
    obs, reward, done, info = env.step(action) # take a random action
env.close()

Variations of environment

MassSpringDamper env

Two states: position and velocity. Goal position is constant.

MassSpringDamper env 2

Three states: position, velocity, and distance to goal point. Goal position is random.

MassSpringDamper env 3v

Three states: position, velocity, and distance to goal point. Goal position is random, zero velocity desired.

MassSpringDamper full env

Four states : position, velocity, position goal, and desired speed at goal position.