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openai
gym
Python2016–2025
openai / gym · Archived

gym

A toolkit for developing and comparing reinforcement learning algorithms.

AI & Machine LearningLearning & ReferenceLibraries & SDKs
Explore on GitHubRead Hacker News discussions
11Distinct people
11Mentions
0Explicit recommenders
37.2KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

A toolkit for developing and comparing reinforcement learning algorithms.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 11:34:28 AM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year11 dated mentions

Observed mentions: 2016-06-19 06:55:48 UTC to 2025-04-12 08:50:13 UTC.

0122016-01-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments20162017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments20202021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20162016-01-01 00:00:00 UTC2017-01-01 00:00:00 UTC211
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC101
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC110
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC101
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC000
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC202
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC000
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC202
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC101
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC101

What Hacker News says

2 sources
pveierland·Dec 5, 2016
It looks like the image from the server and control information to the server is sent through the VNC protocol. Other information such as the reward signal from the environment server is sent through a WebSockets protocol using JSON: https://github.com/openai/universe/blob/master/doc/protocols... You should be able to implement this protocol for your environment and run a VNC server for the rest. A new class for the client representing your environment can be based on this: https://github.com/openai/universe/blob/master/universe/envs... Then register the class with OpenAI Gym: https://github.com/openai/universe/blob/master/universe/__in... After creating the environment using gym.make you need to add information about your remote in the call to configure: env = gym.make('gtav.SaneDriving-v0') env.configure(remotes="vnc://localhost:vnc_por
Read the rest
t+rewarder_port") https://github.com/openai/gym/blob/master/gym/core.py#L234 https://github.com/openai/universe/blob/master/universe/envs... This is only based on a cursory reading, but it should be possible to use custom environments with OpenAI Universe as it is today.
In OpenAI Universe

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