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Title: GitHub - mohammadzainabbas/Reinforcement-Learning-CS: πŸ’‘ Grasp - Pick-and-place with a robotic hand πŸ‘¨πŸ»β€πŸ’» Β· GitHub

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Description: πŸ’‘ Grasp - Pick-and-place with a robotic hand πŸ‘¨πŸ»β€πŸ’». Contribute to mohammadzainabbas/Reinforcement-Learning-CS development by creating an account on GitHub.

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X Description: πŸ’‘ Grasp - Pick-and-place with a robotic hand πŸ‘¨πŸ»β€πŸ’». Contribute to mohammadzainabbas/Reinforcement-Learning-CS development by creating an account on GitHub.

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https://github.com/mohammadzainabbas/Reinforcement-Learning-CS#-grasp---pick-and-place-with-a-robotic-hand-‍
herehttp://mohammadzainabbas.tech/Reinforcement-Learning-CS/
https://github.com/mohammadzainabbas/Reinforcement-Learning-CS#table-of-contents
πŸš€ Quickstart πŸ’»https://github.com/mohammadzainabbas/Reinforcement-Learning-CS#quickstart
πŸ’» Introduction πŸ‘¨πŸ»β€πŸ’»https://github.com/mohammadzainabbas/Reinforcement-Learning-CS#introduction
🌊 Physics Simulation Engines 🦿https://github.com/mohammadzainabbas/Reinforcement-Learning-CS#physics-simulation-engines
πŸŒͺ Environment 🦾https://github.com/mohammadzainabbas/Reinforcement-Learning-CS#environment
πŸ”­ Observations πŸ”https://github.com/mohammadzainabbas/Reinforcement-Learning-CS#observations
πŸ„β€β™‚οΈ Actions πŸ€Έβ€β™‚οΈhttps://github.com/mohammadzainabbas/Reinforcement-Learning-CS#actions
πŸ† Reward πŸ₯‡https://github.com/mohammadzainabbas/Reinforcement-Learning-CS#reward
πŸ”¬ Algorithms πŸ’»https://github.com/mohammadzainabbas/Reinforcement-Learning-CS#algorithms
πŸ’‘ Proximal policy optimization (PPO) πŸ‘¨πŸ»β€πŸ’»https://github.com/mohammadzainabbas/Reinforcement-Learning-CS#ppo
πŸ’‘ Evolution Strategy (ES) πŸ‘¨πŸ»β€πŸ’»https://github.com/mohammadzainabbas/Reinforcement-Learning-CS#es
πŸ’‘ Augmented Random Search (ARS) πŸ‘¨πŸ»β€πŸ’»https://github.com/mohammadzainabbas/Reinforcement-Learning-CS#ars
πŸ’‘ Soft Actor-Critic (SAC) πŸ‘¨πŸ»β€πŸ’»https://github.com/mohammadzainabbas/Reinforcement-Learning-CS#sac
πŸš€ Run locally πŸ–²οΈhttps://github.com/mohammadzainabbas/Reinforcement-Learning-CS#run-locally
https://github.com/mohammadzainabbas/Reinforcement-Learning-CS#1--quickstart-
Grasp: Pick-and-place with a robotic handhttps://colab.research.google.com/github/mohammadzainabbas/Reinforcement-Learning-CS/blob/main/notebooks/demo.ipynb
algorithmshttps://github.com/mohammadzainabbas/Reinforcement-Learning-CS#algorithms
https://github.com/mohammadzainabbas/Reinforcement-Learning-CS/blob/main/docs/assets/compare_algorithms.jpeg?raw=true
Step-by-step training with PPOhttps://colab.research.google.com/github/mohammadzainabbas/Reinforcement-Learning-CS/blob/main/notebooks/demo_ppo_train.ipynb
https://github.com/mohammadzainabbas/Reinforcement-Learning-CS#2--introduction-‍
Brax physics simulation enginehttps://arxiv.org/pdf/2106.13281.pdf
https://github.com/mohammadzainabbas/Reinforcement-Learning-CS/blob/main/docs/assets/figure_1.jpeg?raw=true
https://github.com/mohammadzainabbas/Reinforcement-Learning-CS#3--physics-simulation-engines-
MuJoCohttps://mujoco.org/
dm_controlhttps://github.com/deepmind/dm_control/
Gymhttps://www.gymlibrary.dev/
Gymnasiumhttps://gymnasium.farama.org/
TinyDiffSimhttps://github.com/erwincoumans/tiny-differentiable-simulator
DiffTaichihttps://github.com/taichi-dev/difftaichi
Nimblehttps://github.com/keenon/nimblephysics
PyBullethttps://github.com/bulletphysics/bullet3
Braxhttps://github.com/google/brax/
Braxhttps://github.com/google/brax/
its highly scalable and parallelizable architecturehttps://ai.googleblog.com/2021/07/speeding-up-reinforcement-learning-with.html
https://github.com/mohammadzainabbas/Reinforcement-Learning-CS#4--environment-
grasping environment provided by Braxhttps://github.com/google/brax/blob/198dee3ac4/brax/envs/grasp.py#L25-L1297
https://github.com/mohammadzainabbas/Reinforcement-Learning-CS/blob/main/docs/assets/figure_2.jpeg?raw=true
4.2.2https://arxiv.org/pdf/2106.13281.pdf
https://github.com/mohammadzainabbas/Reinforcement-Learning-CS#41--observations-
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https://github.com/mohammadzainabbas/Reinforcement-Learning-CS#42-‍️-actions-‍️
https://github.com/mohammadzainabbas/Reinforcement-Learning-CS#43--reward-
reward functionhttps://github.com/google/brax/blob/198dee3ac4/brax/envs/grasp.py#L90-L121
https://github.com/mohammadzainabbas/Reinforcement-Learning-CS#5--algorithms-
https://github.com/mohammadzainabbas/Reinforcement-Learning-CS#51--proximal-policy-optimization-ppo-‍
Proximal Policy Optimization (PPO)https://arxiv.org/abs/1707.06347
A2Chttps://huggingface.co/blog/deep-rl-a2c
https://github.com/mohammadzainabbas/Reinforcement-Learning-CS#52--evolution-strategy-es-‍
Evolution Strategy (ES)https://arxiv.org/abs/1707.06347
https://github.com/mohammadzainabbas/Reinforcement-Learning-CS#53--augmented-random-search-ars-‍
Augmented Random Search (ARS)https://arxiv.org/abs/1803.07055
https://github.com/mohammadzainabbas/Reinforcement-Learning-CS#54--soft-actor-critic-sac-‍
Soft Actor-Critic (SAC)https://arxiv.org/abs/1801.01290
https://github.com/mohammadzainabbas/Reinforcement-Learning-CS#6--run-locally-️
install mambahttps://mamba.readthedocs.io/en/latest/installation.html
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herehttp://mohammadzainabbas.tech/Reinforcement-Learning-CS/
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python https://github.com/topics/python
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