softlearning VS awesome-deep-trading

Compare softlearning vs awesome-deep-trading and see what are their differences.

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. (by rail-berkeley)
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softlearning awesome-deep-trading
4 37
1,157 1,390
2.7% -
0.0 0.0
5 months ago 9 months ago
Python
GNU General Public License v3.0 or later MIT License
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softlearning

Posts with mentions or reviews of softlearning. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-09.

awesome-deep-trading

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

What are some alternatives?

When comparing softlearning and awesome-deep-trading you can also consider the following projects:

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

OctoBot - Open source crypto trading bot

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.

tmrl - Reinforcement Learning for real-time applications - host of the TrackMania Roborace League

wolfinch - Wolfinch is your friendly trader Bot written in Python

rl-baselines3-zoo - A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.

awesome-quant - A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)

LiDAR-Guide - LiDAR Guide

Awesome-System-for-Machine-Learning - A curated list of research in machine learning systems (MLSys). Paper notes are also provided.

trax - Trax — Deep Learning with Clear Code and Speed

senza - Experiments with drone control and reinforcement learning.