Coursera_Reinforcement_Learning
d3rlpy
| Coursera_Reinforcement_Learning | d3rlpy | |
|---|---|---|
| 1 | 2 | |
| 197 | 1,197 | |
| - | - | |
| 10.0 | 9.1 | |
| over 4 years ago | 18 days ago | |
| Jupyter Notebook | Python | |
| - | MIT License |
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Coursera_Reinforcement_Learning
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Python libraries for solving reinforcement learning problems implemented in OpenAI gym
I meant State Of The Art (SOTA) ;) Look here for a simple implementation of Expected Sarsa ( you can also find sarsa on github) https://github.com/LucasBoTang/Coursera_Reinforcement_Learning/blob/master/02Sample-based_Learning_Methods/02Q-Learning_and_Expected_Sarsa.ipynb
d3rlpy
- Python libraries for solving reinforcement learning problems implemented in OpenAI gym
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Conservative Q Learning TD error not converging
Hi, I am using the discrete conservative Q learning implementation in the d3rlpy library (https://github.com/takuseno/d3rlpy) to train a policy offline to optimize mechanical ventilation treatment by using the MIMIC-III dataset (https://physionet.org/content/mimiciii-demo/1.4/).
What are some alternatives?
rlai - This is a Python implementation of concepts and algorithms described in "Reinforcement Learning: An Introduction" (Sutton and Barto, 2018, 2nd edition).
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
exorl - ExORL: Exploratory Data for Offline Reinforcement Learning
Minari - A standard format for offline reinforcement learning datasets, with popular reference datasets and related utilities
pytorch-a2c-ppo-acktr-gail - PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).