deep-RL-trading
DeepRL-TensorFlow2
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| deep-RL-trading | DeepRL-TensorFlow2 | |
|---|---|---|
| 14 | 2 | |
| 342 | 573 | |
| - | - | |
| 0.0 | 0.0 | |
| almost 3 years ago | almost 2 years ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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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
DeepRL-TensorFlow2
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PPO implementation in TensorFlow2
I've been searching for a clean, good, and understandable implementation of PPO for continuous action space with TF2 witch is understandable enough for me to apply my modifications, but the closest thing that I have found is this code which seems to not work properly even on a simple gym cartpole env (discussed issues in git-hub repo suggest the same problem) so I have some doubts :). I was wondering whether you could recommend an implementation that you trust and suggest :)
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Question about using tf.stop_gradient in separate Actor-Critic networks for A2C implementation for TF2
I have been looking at this implementation of A2C. Here the author of the code uses stop_gradient only on the critic network at L90 bur not in the actor network L61 for the continuous case. However , it is used both in actor and critic networks for the discrete case. Can someone explain me why?
What are some alternatives?
muzero-general - MuZero
soft-actor-critic - Re-implementation of Soft-Actor-Critic (SAC) in TensorFlow 2.0
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.
TensorFlow2.0-for-Deep-Reinforcement-Learning - TensorFlow 2.0 for Deep Reinforcement Learning. :octopus:
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.
tensorforce - Tensorforce: a TensorFlow library for applied 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.
ydata-synthetic - Synthetic data generators for tabular and time-series data
minimalRL - Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)
deep-q-learning - Minimal Deep Q Learning (DQN & DDQN) implementations in Keras
machin - Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...