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Creating a New Env

This tutorial explains how to create, register, and use a new EvoGym environment. Completed code can be found here.

Page Outline

  1. Introduction
  2. Designing the Environment
  3. File Structure
  4. Writing the Environment Class
    1. Init
    2. Step
    3. Reset
  5. Registering the Environment
  6. Visualization Script


Let’s get started! We’re going to be making an environment for the “walking” task, similar to Walker-v0. This tutorial assumes that you have followed setup instructions for both EvoGym and the Evolution Gym Design Tool.

Designing the Environment

We can specify the voxel structure of our new environment with the Evolution Gym Design Tool. The Design Tool enables us to design environments visually, instead of specifying all objects in code.

Since we’re designing a “walking” task, our environment needs two objects: a robot and a ground. In the Design Tool, we’ll just create the ground object – we will add the robot object in code because we want it to be highly customizable.

Launch the Design Tool from its repo’s main folder:

python src/

Follow the steps to create your “walking” environment:

  1. In the right panel, update the Grid Size to (width = 30, height = 1)
  2. In the right panel, switch the Edit Mode to Voxel Mode and select Fixed Voxel from the dropdown Voxel Selector
  3. In the left panel, fill in all the grid cells with fixed voxels by clicking on them and dragging your cursor. This object will be the ground
  4. Change the Edit Mode to Select Mode and select the ground object. Name it ground in the panel on the right
  5. In the right panel, name the environment simple_walker_env.json and hit save

The result should look something like this:

File Structure

The file simple_walker_env.json should be saved in the exported folder in the Design Tool repo. Create the following file structure in the directory of your choice:

├── envs
│   ├──
│   └──
├── world_data
│   └── simple_walker_env.json

In we will write the code for our environment and specify the observation, reward, and action space. In we will register the environment. Finally, we will see our environment in action in

Writing the Environment Class

The majority of the work in creating a new environment comes in writing the environment class which we will do in Careful consideration should go into designing your environment’s observation and reward as these can significantly impact the performance of co-design or control optimization algorithms you intend to run. The structure of our environment class will be as follows:

from gym import spaces
from evogym import EvoWorld
from evogym.envs import EvoGymBase

import numpy as np
import os

class SimpleWalkerEnvClass(EvoGymBase):

    def __init__(self, body, connections=None):

    def step(self, action):
        return obs, reward, done, {}

    def reset(self):
        return obs

Note that our environment class, SimpleWalkerEnvClass, inherits from EvoGymBase, the superclass of all EvoGym environments.


In the init method, the body and connections of the robot we would like to simulate are passed in. We initialize an EvoWorld object with data from world_data/simple_walker_env.json, and also add our robot to position (1, 1) of the EvoWorld. We pass the completed EvoWorld object to initialize the superclass EvoGymBase. = EvoWorld.from_json(os.path.join('world_data', 'simple_walker_env.json'))'robot', body, 1, 1, connections=connections)

Next, we need to set the observation and action space for our environment. By bounding the action space to [0.6, 1.6], we limit the compression and expansion of each of the robot’s actuators to 60% and 160% of their original size, respectively.

num_actuators = self.get_actuator_indices('robot').size
obs_size = self.reset().size

self.action_space = spaces.Box(low= 0.6, high=1.6, shape=(num_actuators,), dtype=np.float)
self.observation_space = spaces.Box(low=-100.0, high=100.0, shape=(obs_size,), dtype=np.float)

Finally, each EvoGym environment comes with a built-in viewer. We set it to track the robot object to make the visualization look pretty :D



In the step method, we need to step the simulation and compute actions and rewards. We start by stepping the simulation with the robot’s action. Notice that we collect information about the position of our robot before and after the step.

pos_1 = self.object_pos_at_time(self.get_time(), "robot")
done = super().step({'robot': action})
pos_2 = self.object_pos_at_time(self.get_time(), "robot")

Also notice that super().step() returns a done flag. When this is True, the simulation has reached an unstable state from which it cannot recover. We will handle this in the reward computation.

To compute the robot’s reward, we subtract the x-position of the robot’s center of mass before and after stepping the simulation. This way, the robot is rewarded for moving in the positive x direction. Note that pos_1 and pos_2 contain position information about each point in the robot object, so we take an average to get the position of the robot’s center of mass.

com_1 = np.mean(pos_1, 1)
com_2 = np.mean(pos_2, 1)
reward = (com_2[0] - com_1[0])

Next, we handle special cases in the reward computation. A large negative reward is returned whenever the simulation enters a terminal state, and a large positive reward is returned if the robot achieves the goal. In this case, the goal is for the robot reach the end of the ground object, which is 30 voxels long.

if done:
	reward -= 3.0
if com_2[0] > 28:
	done = True
	reward += 1.0

Finally, we compute the observation and return all relevant data. In this example, the returned observation consists of two parts. get_vel_com_obs returns the velocity of the robot’s center of mass. get_relative_pos_obs returns the positions of the robots’s point masses relative to the robot’s center of mass. You can read more about built-in observation helper functions here.

obs = np.concatenate((
return obs, reward, done, {}

The last return value is for debugging information.


In the reset method, we first reset the simulation. Then we return the same observation as done in step.

obs = np.concatenate((
return obs

Registering the Environment

We register the environment in using functionality provided by OpenAI Gym. In this file, we can register many environments by calling register for each environment as desired, but in this example we just register one.

from envs.simple_env import SimpleWalkerEnvClass
from gym.envs.registration import register

    id = 'SimpleWalkingEnv-v0',
    entry_point = 'envs.simple_env:SimpleWalkerEnvClass',
    max_episode_steps = 500

In the registration, SimpleWalkingEnv-v0 is our environment’s name and we’ve set it to run for 500 steps before resetting. entry_point tells gym where to find the class specification of our environment.

Visualization Script

We’re almost there! We will write the visualization script in We use evogym.sample_robot to sample a random 5x5 robot and gym.make to create an instance of SimpleWalkingEnv-v0 with our sampled robot. We step the environment 500 times. In each iteration, we sample a random action vector for our robot, step the environment, and render it.

import gym
from evogym import sample_robot

# // import envs from the envs folder and register them
import envs

if __name__ == '__main__':

    # // create a random robot
    body, connections = sample_robot((5,5))

    # // make the SimpleWalkingEnv using gym.make and with the robot information
    env = gym.make('SimpleWalkingEnv-v0', body=body)

    # // step the environment for 500 iterations
    for i in range(500):
        action = env.action_space.sample()
        ob, reward, done, info = env.step(action)
        if done:

From within the directory containing run


and enjoy!