minimalRL VS Fleet-AI

Compare minimalRL vs Fleet-AI and see what are their differences.

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minimalRL Fleet-AI
5 1
2,725 3
- -
1.6 0.0
about 1 year ago over 2 years ago
Python Python
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.

minimalRL

Posts with mentions or reviews of minimalRL. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-07-18.

Fleet-AI

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

What are some alternatives?

When comparing minimalRL and Fleet-AI you can also consider the following projects:

ElegantRL - Massively Parallel Deep Reinforcement Learning. 🔥

DeepRL-TensorFlow2 - 🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2

Pytorch-PCGrad - Pytorch reimplementation for "Gradient Surgery for Multi-Task Learning"

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

pomdp-baselines - Simple (but often Strong) Baselines for POMDPs in PyTorch, ICML 2022

rlpyt - Reinforcement Learning in PyTorch

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).

autonomous-learning-library - A PyTorch library for building deep reinforcement learning agents.

deep-RL-trading - playing idealized trading games with deep reinforcement learning