fred
policy-adaptation-during-deployment
| fred | policy-adaptation-during-deployment | |
|---|---|---|
| 3 | 1 | |
| 92 | 109 | |
| - | - | |
| 2.8 | 1.8 | |
| 9 days ago | over 3 years ago | |
| Python | Python | |
| GNU General Public License v3.0 only | - |
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fred
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Anyone know of any good robotic arm programming simulators?
Another option: pybullet. If you just want to learn and you don't need a super high performant system go with python. As an example my code for controlling a simulated robot arm with pybullet my code
- Fred the Robot Arm – 6 DOF 3D printed dynamixel robot arm
- Obstacle avoidance using deep reinforcement learning on a 3d printed 6 DOF robot arm. Github in comments.
policy-adaptation-during-deployment
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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?
trax - Trax — Deep Learning with Clear Code and Speed
Ne2Ne-Image-Denoising - Deep Unsupervised Image Denoising, based on Neighbour2Neighbour training
gazebo-classic - Gazebo classic. For the latest version, see https://github.com/gazebosim/gz-sim
envpool - C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
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).
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
drq - DrQ: Data regularized Q