Minari VS d3rlpy

Compare Minari vs d3rlpy 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 d3rlpy
1 2
219 1,207
5.9% -
8.2 9.1
3 days ago 6 days 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.
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Minari

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

d3rlpy

Posts with mentions or reviews of d3rlpy. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-01-20.

What are some alternatives?

When comparing Minari and d3rlpy you can also consider the following projects:

exorl - ExORL: Exploratory Data for Offline Reinforcement Learning

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

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

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

Coursera_Reinforcement_Learning - Coursera Reinforcement Learning Specialization by University of Alberta & Alberta Machine Intelligence Institute

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

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).

rlai - This is a Python implementation of concepts and algorithms described in "Reinforcement Learning: An Introduction" (Sutton and Barto, 2018, 2nd edition).