Deep-Reinforcement-Learning-Algorithms
autonomous-learning-library
| Deep-Reinforcement-Learning-Algorithms | autonomous-learning-library | |
|---|---|---|
| 3 | 2 | |
| 903 | 648 | |
| 0.0% | 0.0% | |
| 3.6 | 7.6 | |
| about 4 years ago | over 1 year ago | |
| Jupyter Notebook | Python | |
| - | 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-Reinforcement-Learning-Algorithms
-
Is there a canonical simple "helloworld" neural network design? Something beyond AND/OR logic, a handful of nodes that does something mildly "useful"?
I guess the most spectacular in terms of performance/"brain size" ratio is a 2 neuron, 8 weights network https://github.com/Rafael1s/Deep-Reinforcement-Learning-Algorithms/tree/master/CartPole-Policy-Based-Hill-Climbing
-
Training time of CartPole is way to long
It can be solved in 113 episodes by Hill Climbing algorithm, https://github.com/Rafael1s/Deep-Reinforcement-Learning-Algorithms/tree/master/CartPole-Policy-Based-Hill-Climbingor by Double Deep Q-Learning in 612 episodes, https://github.com/Rafael1s/Deep-Reinforcement-Learning-Algorithms/tree/master/Cartpole-Double-Deep-Q-Learning
-
Need help with PyTorch script for Actor_Critic implementation of MountainCar env.
You can find the solution for MountainCar env here: https://github.com/Rafael1s/Deep-Reinforcement-Learning-Algorithms/tree/master/MountainCarContinuous-TD3This solution implemented using PyTorch. The TD3 model is the successor to DDPG algorithm using the Actor-Critic model.
autonomous-learning-library
-
What's the best "Non-Black Box" framework for SOTA algorithms?
I find Autonomous Learning Library well-designed and clean, despite its modularity to some degree.
-
Where do people get their algorithm implementations from?
I very strongly recommend the autonomous learning library: https://github.com/cpnota/autonomous-learning-library
What are some alternatives?
Popular-RL-Algorithms - PyTorch implementation of Soft Actor-Critic (SAC), Twin Delayed DDPG (TD3), Actor-Critic (AC/A2C), Proximal Policy Optimization (PPO), QT-Opt, PointNet..
Tetris-deep-Q-learning-pytorch - Deep Q-learning for playing tetris game
Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020 - Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy. ICAIF 2020. Please star. [Moved to: https://github.com/AI4Finance-Foundation/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020]
learning-to-drive-in-5-minutes - Implementation of reinforcement learning approach to make a car learn to drive smoothly in minutes
rl_lib - Series of deep reinforcement learning algorithms 🤖
Meta-SAC - Auto-tune the Entropy Temperature of Soft Actor-Critic via Metagradient - 7th ICML AutoML workshop 2020