policy-adaptation-during-deployment VS drq

What are some alternatives?

Ne2Ne-Image-Denoising - Deep Unsupervised Image Denoising, based on Neighbour2Neighbour training

exorl - ExORL: Exploratory Data for Offline Reinforcement Learning

envpool - C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.

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).

stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

muzero-general - MuZero

cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)

drl_grasping - Deep Reinforcement Learning for Robotic Grasping from Octrees

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.

dmc2gymnasium - Gymnasium integration for the DeepMind Control (DMC) suite

es_pytorch - High performance implementation of Deep neuroevolution in pytorch using mpi4py. Intended for use on HPC clusters