seqeval
gym
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| seqeval | gym | |
|---|---|---|
| 1 | 96 | |
| 1,044 | 33,846 | |
| 1.3% | 0.8% | |
| 0.0 | 0.0 | |
| 3 months ago | 13 days ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
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seqeval
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Beginner questions about NER model evaluation.
. The standard way to evaluate NER (or any other sequence labelling problem) is to use the conlleval script (https://www.clips.uantwerpen.be/conll2000/chunking/output.html) or through the seqeval package in python (https://github.com/chakki-works/seqeval) . Either way, you need a list of predicted labels and a list of gold labels (see the code example in the link, it should be trivial to converse your output to the same data format).
gym
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OpenAI Acquires Global Illumination
A co-founder announced they disbanded their robots team a couple years ago: https://venturebeat.com/business/openai-disbands-its-robotic...
That was the same time they depreciated OpenAI Gym: https://github.com/openai/gym
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Shimmy 1.0: Gymnasium & PettingZoo bindings for popular external RL environments
This includes single-agent Gymnasium wrappers for DM Control, DM Lab, Behavior Suite, Arcade Learning Environment, OpenAI Gym V21 & V26. Multi-agent PettingZoo wrappers support DM Control Soccer, OpenSpiel and Melting Pot. For more information, read the release notes here:
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Some confusion about variables and functions in mujoco-py
When I browse fetch_env.py, I have a question about the following code snippet:
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pip install stable-baselines3[extra]
Nvm, this works for me '!pip install setuptools==65.5.0' Source: https://github.com/openai/gym/issues/3176
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[P] Reinforcement learning evolutionary hyperparameter optimization - 10x speed up
how would this interact/compare with https://github.com/openai/gym?
- What has replaced OpenAI Retro Gym?
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Understanding Reinforcement Learning
If you'd like to learn more about reinforcement learning or play with a number of samples in controlled environments, I highly recommend you look at the documentation for OpenAI's Gym library and particularly the basic usage page. OpenAI's Gym provides a standardized environment for performing reinforcement learning on classic Atari games and a few other platforms and should be an educational resource. If you'd like a more detailed example, check out this tutorial on Paperspace's blog.
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Using the cross-entropy method to solve Frozen Lake
Frozen Lake is an OpenAI Gym environment in which an agent is rewarded for traversing a frozen surface from a start position to a goal position without falling through any perilous holes in the ice.
- Is there a publicly available state space model for the Lunar Lander environment?
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How to Create a Behavioral Cloning Bot to Play Online Games?
typically a more relaxed approach is taken via reinforcement learning, but it requires that you can simulate the game via a given gamestate. take a look at e.g. https://www.gymlibrary.dev/
What are some alternatives?
scikit-learn - scikit-learn: machine learning in Python
ml-agents - The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.
SciKit-Learn Laboratory - SciKit-Learn Laboratory (SKLL) makes it easy to run machine learning experiments.
carla - Open-source simulator for autonomous driving research.
Metrics - Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave
tensorflow - An Open Source Machine Learning Framework for Everyone
dm_control - Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.
flair - A very simple framework for state-of-the-art Natural Language Processing (NLP)
open_spiel - OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
Keras - Deep Learning for humans
rlcard - Reinforcement Learning / AI Bots in Card (Poker) Games - Blackjack, Leduc, Texas, DouDizhu, Mahjong, UNO.