maze VS RL-Adventure

Compare maze vs RL-Adventure and see what are their differences.

RL-Adventure

Pytorch Implementation of DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchical RL (by higgsfield)
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maze RL-Adventure
4 3
257 2,903
1.2% -
0.0 0.0
22 days ago over 2 years ago
Python Jupyter Notebook
GNU General Public License v3.0 or later -
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.

maze

Posts with mentions or reviews of maze. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-03-12.

RL-Adventure

Posts with mentions or reviews of RL-Adventure. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-07-04.

What are some alternatives?

When comparing maze and RL-Adventure you can also consider the following projects:

dcss-ai-wrapper - An API for Dungeon Crawl Stone Soup for Artificial Intelligence research.

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

machin - Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...

nle - The NetHack Learning Environment

dm_env - A Python interface for reinforcement learning environments

RL-Adventure-2 - Fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters [Moved to: https://github.com/higgsfield-ai/higgsfield]

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.

flux-beamer - Flux is a modern style beamer presentation.