policy-adaptation-during-deployment
drl_grasping
policy-adaptation-during-deployment | drl_grasping | |
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1 | 4 | |
109 | 326 | |
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1.8 | 0.0 | |
over 3 years ago | over 1 year ago | |
Python | Python | |
- | BSD 3-clause "New" or "Revised" License |
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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
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
What are some alternatives?
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robo-gym - An open source toolkit for Distributed Deep Reinforcement Learning on real and simulated robots.
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
rex-gym - OpenAI Gym environments for an open-source quadruped robot (SpotMicro)
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).
habitat-lab - A modular high-level library to train embodied AI agents across a variety of tasks and environments.
dmc2gymnasium - Gymnasium integration for the DeepMind Control (DMC) suite
gym-pybullet-drones - PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control
es_pytorch - High performance implementation of Deep neuroevolution in pytorch using mpi4py. Intended for use on HPC clusters
rlcard - Reinforcement Learning / AI Bots in Card (Poker) Games - Blackjack, Leduc, Texas, DouDizhu, Mahjong, UNO.