drl_grasping
policy-adaptation-during-deployment
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| drl_grasping | policy-adaptation-during-deployment | |
|---|---|---|
| 4 | 1 | |
| 326 | 109 | |
| - | - | |
| 0.0 | 1.8 | |
| over 1 year ago | over 3 years ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | - |
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drl_grasping
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Researchers at The University of Luxembourg Develop a Method to Learn Grasping Objects on the Moon from 3D Octree Observations with Deep Reinforcement Learning
Continue reading| Check out the paper,and github link
- ROS 2 + Ignition + OpenAI Gym Deep RL Example
- ROS 2 + Ignition + OpenAI Gym Tutorial
- Deep Reinforcement Learning for Robotic Grasping from Octrees
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?
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robo-gym - An open source toolkit for Distributed Deep Reinforcement Learning on real and simulated robots.
envpool - C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.
rex-gym - OpenAI Gym environments for an open-source quadruped robot (SpotMicro)
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
habitat-lab - A modular high-level library to train embodied AI agents across a variety of tasks and 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).
gym-pybullet-drones - PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control
dmc2gymnasium - Gymnasium integration for the DeepMind Control (DMC) suite
lodtree - A simple rust library to help create octrees and quadtrees for chunked level of detail
es_pytorch - High performance implementation of Deep neuroevolution in pytorch using mpi4py. Intended for use on HPC clusters