minimalRL VS deep-RL-trading

Compare minimalRL vs deep-RL-trading and see what are their differences.

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minimalRL deep-RL-trading
5 14
2,725 342
- -
1.6 0.0
about 1 year ago almost 3 years 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.

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.

deep-RL-trading

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

What are some alternatives?

When comparing minimalRL and deep-RL-trading you can also consider the following projects:

ElegantRL - Massively Parallel Deep Reinforcement Learning. 🔥

muzero-general - MuZero

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

TradingView-Machine-Learning-GUI - Embark on a trading journey with this project's cutting-edge stop loss/take profit generator, fine-tuning your TradingView strategy to perfection. Harness the power of sklearn's machine learning algorithms to unlock unparalleled strategy optimization and unleash your trading potential.

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

softlearning - Softlearning is a reinforcement learning framework for training maximum entropy policies in continuous domains. Includes the official implementation of the Soft Actor-Critic algorithm.

rlpyt - Reinforcement Learning in PyTorch

Note - Easily implement parallel training and distributed training. Machine learning library. Note.neuralnetwork.tf package include Llama2, Llama3, CLIP, ViT, ConvNeXt, SwiftFormer, etc, these models built with Note are compatible with TensorFlow and can be trained with TensorFlow.

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

ultimate-volleyball - 3D RL Volleyball environment built on Unity ML-Agents

deep-q-learning - Minimal Deep Q Learning (DQN & DDQN) implementations in Keras