d3rlpy
Minari
| d3rlpy | Minari | |
|---|---|---|
| 2 | 1 | |
| 1,215 | 222 | |
| - | 7.2% | |
| 9.1 | 8.2 | |
| 10 days ago | 10 days ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
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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/).
Minari
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Announcing Minari (Gym for offline RL, by the Farama Foundation) is going into public beta
You can also read the full release notes here: https://github.com/Farama-Foundation/Minari/releases/tag/v0.3.0
What are some alternatives?
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
gymprecice - A framework to design and develop reinforcement learning environments for single- and multi-physics active flow control.
Coursera_Reinforcement_Learning - Coursera Reinforcement Learning Specialization by University of Alberta & Alberta Machine Intelligence Institute
PettingZoo - An API standard for multi-agent reinforcement learning environments, with popular reference environments 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).
MO-Gymnasium - Multi-objective Gymnasium environments for reinforcement learning
rlai - This is a Python implementation of concepts and algorithms described in "Reinforcement Learning: An Introduction" (Sutton and Barto, 2018, 2nd edition).