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PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKT…
Official implementation of NeurIPS'23 paper, Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets
A collection of comprehensive notes on Deep Reinforcement Learning, customized for UC Berkeley's CS 285 (prev. CS 294-112)
High-quality single-file implementations of SOTA Offline and Offline-to-Online RL algorithms: AWAC, BC, CQL, DT, EDAC, IQL, SAC-N, TD3+BC, LB-SAC, SPOT, Cal-QL, ReBRAC
An offline deep reinforcement learning library
JAX (Flax) implementation of algorithms for Deep Reinforcement Learning with continuous action spaces.
S3D Text-Video model trained on HowTo100M using MIL-NCE
🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gan…
Free course that takes you from zero to Reinforcement Learning PRO 🦸🏻🦸🏽
This is the homepage of a new book entitled "Mathematical Foundations of Reinforcement Learning."
A2C is a special case of PPO!
Pilot course for Robotics 101: Computational Linear Algebra
Python interface to control Franka Emika Panda Research Robot Arms.
Simulation and robot code for contact-rich household object insertion (ICRA 2023).
A CLI for processing composite Wavefront OBJ files for use in MuJoCo.
A collection of high-quality models for the MuJoCo physics engine, curated by Google DeepMind.
ROS integration for Franka research robots
Use ChatGPT to summarize the arXiv papers. 全流程加速科研，利用chatgpt进行论文全文总结+专业翻译+润色+审稿+审稿回复
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
Massively Parallel Deep Reinforcement Learning. 🔥
PyTorch Tutorial for Deep Learning Researchers
A collection of reference environments for offline reinforcement learning