The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning. Learn more →
Rliable Alternatives
Similar projects and alternatives to rliable based on common topics and language
-
CARLA
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms (by carla-recourse)
-
InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
-
bsuite
bsuite is a collection of carefully-designed experiments that investigate core capabilities of a reinforcement learning (RL) agent
-
cleverhans
An adversarial example library for constructing attacks, building defenses, and benchmarking both
-
dopamine
Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better rliable alternative or higher similarity.
rliable reviews and mentions
Posts with mentions or reviews of rliable.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-10-04.
-
[D] What is standard practice in RL when reporting average returns across multiple seeds in a table or a plot?
You can also look up https://github.com/google-research/rliable https://arxiv.org/pdf/2108.13264.pdf (Neurips 21 outstanding paper) IMHO the field would benefit if its moves in that direction.
-
What is the next booming topic in Deep RL?
You might like the best paper at NeurIPS last year: https://agarwl.github.io/rliable/
-
"Human-level Atari 200x faster", DeepMind 2022 (200x reduction in dataset scale required by Agent57 for human performance)
Deep RL at the Edge of the Statistical Precipice https://agarwl.github.io/rliable/
- How Hugging Face đŸ¤— can contribute to the Deep Reinforcement Learning Ecosystem?
-
Deep RL at the Edge of Statistical Precipice (NeurIPS Outstanding Paper)
You can find the paper, slides and poster at agarwl.github.io/rliable. The OP already put the poster here.
-
Google Highlights How Statistical Uncertainty Of Outcomes Must Be Considered To Evaluate Deep RL Reliably and Propose A Python Library Called ‘RLiable’
A recent Google study highlights how statistical uncertainty of outcomes must be considered for deep RL evaluation to be reliable, especially when only a few training runs are used. Google has also released an easy-to-use Python library called RLiable to help researchers incorporate these tools.
-
[R] Rliable: Better Evaluation for Reinforcement Learning—A Visual Explanation
Website: https://agarwl.github.io/rliable/
-
Towards creating better reward functions in a custom environment| Sensitivity analysis [Question]
First off, "performance" is highly speculative. So make sure you nail down what you mean and ensure reliability of those measurements. Check out https://github.com/google-research/rliable.
- Deep Reinforcement Learning at the Edge of the Statistical Precipice
-
Best RL papers from the past year or two?
Our NeurIPS'21 oral on Deep RL at the Edge of the Statistical Precipice would make for a fun read :)
-
A note from our sponsor - WorkOS
workos.com | 23 Apr 2024
Stats
Basic rliable repo stats
15
694
2.5
28 days ago
google-research/rliable is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of rliable is Jupyter Notebook.
Popular Comparisons
Sponsored
SaaSHub - Software Alternatives and Reviews
SaaSHub helps you find the best software and product alternatives
www.saashub.com