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openai
baselines
Python2017–2021
openai / baselines

baselines

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

3Distinct people
6Mentions
0Explicit recommenders
16.8KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

OpenAI Baselines: high-quality implementations of 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

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This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 1:49:46 PM 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 year6 dated mentions

Observed mentions: 2017-08-27 07:39:56 UTC to 2021-09-11 20:26:34 UTC.

0122017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments20172018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments20192020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2021

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
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC202
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC101
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC000
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC101
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC202

What Hacker News says

6 sources
AutoML is RL? The entire exercise of publishing and peer review is an exercise in cybernetics? https://en.wikipedia.org/wiki/Probabilistic_logic_network : > The basic goal of PLN is to provide reasonably accurate probabilistic inference in a way that is compatible with both term logic and predicate logic, and scales up to operate in real time on large dynamic knowledge bases. > The goal underlying the theoretical development of PLN has been the creation of practical software systems carrying out complex, useful inferences based on uncertain knowledge and drawing uncertain conclusions. PLN has been designed to allow basic probabilistic inference to interact with other kinds of inference such as intensional inference, fuzzy inference, and higher-order inference using quantifiers, variables, and combinators, and be a more convenient approach
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than Bayesian networks (or other conventional approaches) for the purpose of interfacing basic probabilistic inference with these other sorts of inference. In addition, the inference rules are formulated in such a way as to avoid the paradoxes of Dempster–Shafer theory. Has anybody already taught / reinforced an OpenCog [PLN, MOSES] AtomSpace hypergraph agent to do Linked Data prep and also convex optimization with AutoML and better than grid search so gradients? Perhaps teaching users to bias analyses with e.g. Yellowbrick and the sklearn APIs would be a good curriculum traversal? opening/baselines "Logging and vizualizing learning curves and other training metrics" https://github.com/openai/baselines#logging-and-vizualizing-... https://en.wikipedia.org/wiki/AlphaZero There's probably an awesome-automl by now? Again, the sklearn interfaces. TIL that SymPy supports NumPy, PyTorch, and TensorFlow [Quantum; TFQ?]; and with a Computer Algebra System something for mutating the AST may not be necessary for symbolic expression trees without human-readable comments or symbol names? Lean mathlib: https://github.com/leanprover-community/mathlib , and then reasoning about concurrent / distributed systems (with side channels in actual physical component space) with e.g. TLA+. There are new UUID formats that are timestamp-sortable; for when blockchain cryptographic hashes aren't enough entropy. "New UUID Formats – IETF Draft" https://news.ycombinator.com/item?id=28088213 ... You can host online ML algos through SingularityNet, which also does PayPal now for the RL.
In Interpretable Model-Based Hierarchical RL Using Inductive Logic Programming

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