rliable
dopamine
| rliable | dopamine | |
|---|---|---|
| 15 | 3 | |
| 699 | 10,375 | |
| 1.7% | 0.1% | |
| 2.5 | 4.8 | |
| about 1 month ago | about 1 month ago | |
| Jupyter Notebook | Jupyter Notebook | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
rliable
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[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.
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What is the next booming topic in Deep RL?
You might like the best paper at NeurIPS last year: https://agarwl.github.io/rliable/
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"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 š¤ can contribute to the Deep Reinforcement Learning Ecosystem?
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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.
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Google Highlights How Statistical Uncertainty Of Outcomes Must Be Considered To Evaluate Deep RL Reliably and Propose A Python Library Called āRLiableā
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.
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[R] Rliable: Better Evaluation for Reinforcement LearningāA Visual Explanation
Website: https://agarwl.github.io/rliable/
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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
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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 :)
dopamine
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Fast and hackable frameworks for RL research
I'm tired of having my 200m frames of Atari take 5 days to run with dopamine, so I'm looking for another framework to use. I haven't been able to find one that's fast and hackable, preferably distributed or with vectorized environments. Anybody have suggestions? seed-rl seems promising but is archived (and in TF2). sample-factory seems super fast but to the best of my knowledge doesn't work with replay buffers. I've been trying to get acme working but documentation is sparse and many of the features are broken.
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RL review
You can also reference the source code for some of the popular implementations from open source RL libraries like stablebaselines3, RLlib, CleanRL, or Dopamine. These can help you if youāre trying to compare your implementation to a āstandardā.
- Rainbow Library
What are some alternatives?
CARLA - CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
SuiSense - Using Artificial Intelligence to distinguish between suicidal and depressive messages (4th Place Congressional App Challenge)
iris - Transformers are Sample-Efficient World Models. ICLR 2023, notable top 5%.
imodels - Interpretable ML package š for concise, transparent, and accurate predictive modeling (sklearn-compatible).
cleverhans - An adversarial example library for constructing attacks, building defenses, and benchmarking both
airline-sentiment-streaming - Streaming with Airline Sentiment. Utilizing Cloudera Machine Learning, Apache NiFi, Apache Hue, Apache Impala, Apache Kudu
nlpaug - Data augmentation for NLP
CodeSearchNet - Datasets, tools, and benchmarks for representation learning of code.
ai-traineree - PyTorch agents and tools for (Deep) Reinforcement Learning
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
creative-prediction - Creative Prediction with Neural Networks
lmgtfy - A "Let Me Google That For You" clone that's open source and doesn't track you when you share it.