Holodeck

Holodeck is a high-fidelity simulator for reinforcement learning built on top of Unreal Engine 4.

Features

7+ rich worlds for training agents in, and many scenarios for those worlds
Linux and Windows support
Easily extend and modify training scenarios
Train and control more than one agent at once
Simple, OpenAI Gym-like Python interface
High performance - simulation speeds of up to 2x real time are possible. Performance penalty only for what you need
Run headless or watch your agents learn

Questions? Join our Discord!

Installation

pip install holodeck

(requires >= Python 3.5)

See Installation for complete instructions (including Docker).

Documentation

Usage Overview

Holodeck's interface is similar to OpenAI's Gym.

We try and provide a batteries included approach to let you jump right into using Holodeck, with minimal fiddling required.

To demonstrate, here is a quick example using the DefaultWorlds package:

import holodeck

# Load the environment. This environment contains a UAV in a city.
env = holodeck.make("UrbanCity-MaxDistance")

# You must call `.reset()` on a newly created environment before ticking/stepping it
env.reset()                         

# The UAV takes 3 torques and a thrust as a command.
command = [0, 0, 0, 100]   

for i in range(30):
    state, reward, terminal, info = env.step(command)

state: dict of sensor name to the sensor's value (nparray).
reward: the reward received from the previous action
terminal: indicates whether the current state is a terminal state.
info: contains additional environment specific information.

If you want to access the data of a specific sensor, import sensors and retrieving the correct value from the state dictionary:

print(state["LocationSensor"])

Multi Agent-Environments

Holodeck supports multi-agent environments.

Calls to step only provide an action for the main agent, and then tick the simulation.

act provides a persistent action for a specific agent, and does not tick the simulation. After an action has been provided, tick will advance the simulation forward. The action is persisted until another call to act provides a different action.

import holodeck
import numpy as np

env = holodeck.make("CyberPunkCity-Follow")
env.reset()

# Provide an action for each agent
env.act('uav0', np.array([0, 0, 0, 100]))
env.act('nav0', np.array([0, 0, 0]))

# Advance the simulation
for i in range(300):
  # The action provided above is repeated
  states = env.tick()

You can access the reward, terminal and location for a multi agent environment as follows:

task = states["uav0"]["FollowTask"]

reward = task[0]
terminal = task[1]
location = states["uav0"]["LocationSensor"]

(uav0 comes from the scenario configuration file)

Running Holodeck Headless

Holodeck can run headless with GPU accelerated rendering. See Using Holodeck Headless

Citation:

@misc{HolodeckPCCL,
  Author = {Joshua Greaves and Max Robinson and Nick Walton and Mitchell Mortensen and Robert Pottorff and Connor Christopherson and Derek Hancock and Jayden Milne and David Wingate},
  Title = {Holodeck: A High Fidelity Simulator},
  Year = {2018},
}

Holodeck is a project of BYU's Perception, Cognition and Control Lab (https://pcc.cs.byu.edu/).

BYU-PCCL / holodeck

readme