IC3Net VS wandb

Compare IC3Net vs wandb and see what are their differences.

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IC3Net wandb
2 16
202 8,243
0.0% 2.0%
0.0 9.9
7 months ago 5 days ago
Python Python
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

IC3Net

Posts with mentions or reviews of IC3Net. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-04-17.

wandb

Posts with mentions or reviews of wandb. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-02-05.

What are some alternatives?

When comparing IC3Net and wandb you can also consider the following projects:

smac - SMAC: The StarCraft Multi-Agent Challenge

tensorboard - TensorFlow's Visualization Toolkit

Mava - 🦁 A research-friendly codebase for fast experimentation of multi-agent reinforcement learning in JAX

aim - Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.

Emergent-Multiagent-Strategies - Emergence of complex strategies through multiagent competition

stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

malib - A parallel framework for population-based multi-agent reinforcement learning.

guildai - Experiment tracking, ML developer tools

Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

pytorch-summary - Model summary in PyTorch similar to `model.summary()` in Keras

cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)

Tetris-deep-Q-learning-pytorch - Deep Q-learning for playing tetris game