Minari VS exorl

Compare Minari vs exorl and see what are their differences.

Minari

A standard format for offline reinforcement learning datasets, with popular reference datasets and related utilities (by Farama-Foundation)
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Minari exorl
1 1
219 94
5.9% -
8.2 4.3
2 days ago over 2 years ago
Python Python
GNU General Public License v3.0 or later 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.

Minari

Posts with mentions or reviews of Minari. We have used some of these posts to build our list of alternatives and similar projects.

exorl

Posts with mentions or reviews of exorl. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing Minari and exorl you can also consider the following projects:

d3rlpy - An offline deep reinforcement learning library

gymprecice - A framework to design and develop reinforcement learning environments for single- and multi-physics active flow control.

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

PettingZoo - An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities

Super-mario-bros-PPO-pytorch - Proximal Policy Optimization (PPO) algorithm for Super Mario Bros

MO-Gymnasium - Multi-objective Gymnasium environments for reinforcement learning

drq - DrQ: Data regularized Q

pytorch-a2c-ppo-acktr-gail - PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

DI-engine - OpenDILab Decision AI Engine