drq
policy-adaptation-during-deployment
drq | policy-adaptation-during-deployment | |
---|---|---|
1 | 1 | |
398 | 109 | |
- | - | |
0.0 | 1.8 | |
over 1 year ago | over 3 years ago | |
Jupyter Notebook | Python | |
MIT License | - |
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drq
policy-adaptation-during-deployment
-
Exploring Self-Supervised Policy Adaptation To Continue Training After Deployment Without Using Any Rewards
Code: https://github.com/nicklashansen/policy-adaptation-during-deployment
What are some alternatives?
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envpool - C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.
muzero-general - MuZero
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
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.
drl_grasping - Deep Reinforcement Learning for Robotic Grasping from Octrees
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