awesome-deep-trading
softlearning
| awesome-deep-trading | softlearning | |
|---|---|---|
| 37 | 4 | |
| 1,395 | 1,159 | |
| - | 1.8% | |
| 0.0 | 0.0 | |
| 9 months ago | 5 months ago | |
| Python | ||
| MIT License | GNU General Public License v3.0 or later |
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awesome-deep-trading
softlearning
-
Problem with Truncated Quantile Critics (TQC) and n-step learning algorithm.
# see https://github.com/rail-berkeley/softlearning/issues/60
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Infinite Horizon problem with SAC and custom environment
Found relevant code at https://github.com/rail-berkeley/softlearning + all code implementations here
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SAC: Enforcing Action Bounds formula derivation
Code for https://arxiv.org/abs/1812.05905 found: https://github.com/rail-berkeley/softlearning
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DDPG not solving MountainCarContinuous
You may read - issue with SAC (https://github.com/rail-berkeley/softlearning/issues/76 ), solution: use large OU noise or use other type of exploration like gSDE
What are some alternatives?
OctoBot - Open source crypto trading bot
deep-RL-trading - playing idealized trading games with deep reinforcement learning
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.
wolfinch - Wolfinch is your friendly trader Bot written in Python
tmrl - Reinforcement Learning for real-time applications - host of the TrackMania Roborace League
awesome-quant - A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)
rl-baselines3-zoo - A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
Awesome-System-for-Machine-Learning - A curated list of research in machine learning systems (MLSys). Paper notes are also provided.
LiDAR-Guide - LiDAR Guide
trax - Trax — Deep Learning with Clear Code and Speed
senza - Experiments with drone control and reinforcement learning.