deep-q-learning
deep-RL-trading
| deep-q-learning | deep-RL-trading | |
|---|---|---|
| 1 | 14 | |
| 1,209 | 342 | |
| - | - | |
| 0.0 | 0.0 | |
| over 3 years ago | almost 3 years ago | |
| Python | Python | |
| MIT License | MIT License |
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.
deep-q-learning
-
Deep Q Network knapsack problem
So go online on GitHub and find a DQN implementation that has options for using a feedforward net as input (instead of conv net as your input isn’t pixel based). Any remotely modular piece of code will take in state space size and action space as parameters to their NN. This is essentially setting input layer to be equal to state space (so 4) and output layer to be action space (201). (https://github.com/keon/deep-q-learning) this repo seems helpful i a cursory glance
deep-RL-trading
- deep-RL-trading: trading game comparing RNN vs CNN vs MLP based on [paper](https://arxiv.org/abs/1803.03916) Deep Learning And Reinforcement Learning - star count:301.0
- deep-RL-trading: trading game comparing RNN vs CNN vs MLP based on [paper](https://arxiv.org/abs/1803.03916) Deep Learning And Reinforcement Learning - star count:272.0
What are some alternatives?
Tetris-deep-Q-learning-pytorch - Deep Q-learning for playing tetris game
muzero-general - MuZero
chainerrl - ChainerRL is a deep reinforcement learning library built on top of Chainer.
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.
pytorch-learn-reinforcement-learning - A collection of various RL algorithms like policy gradients, DQN and PPO. The goal of this repo will be to make it a go-to resource for learning about RL. How to visualize, debug and solve RL problems. I've additionally included playground.py for learning more about OpenAI gym, etc.
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.
minimalRL - Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)
awesome-deep-trading - List of awesome resources for machine learning-based algorithmic trading
Agar.io_Q-Learning_AI - An experiment on the performance of homemade Q-learning AIs in Agar.io depending on their state representation and available actions