Showing 301-320 of 322 projects
High quality implementations of Deep Reinforcement Learning algorithms written in PyTorch
OmniSafe is an infrastructural framework for accelerating safe reinforcement learning research.
A library for training reusable controllers for physically simulated characters using adversarial skill embeddings.
A Python library for training a humanoid robot to walk using reinforcement learning.
Implementations of selected inverse reinforcement learning algorithms.
A comprehensive guide for developers to stay up-to-date with the latest advancements in AI, ML, DL, and computer vision.
A Python library that provides a Reinforcement Learning environment using the PyGame game engine.
An index of algorithms for offline reinforcement learning (offline-rl) targeting AI and ML researchers.
A Python library for Model-Based Reinforcement Learning research and development.
A Python library for implementing reinforcement learning algorithms.
A collection of Reinforcement Learning algorithms implemented in Python for educational and research purposes.
XuanCe is a comprehensive and unified deep reinforcement learning library for a variety of environments and tasks.
Reinforcement learning environments for the Omniverse Isaac Gym, a robotics simulation platform.
Scalable, event-driven, deep-learning-friendly backtesting library for algorithmic trading strategies
Fast and efficient Jax-based reinforcement learning implementations for developers building AI-powered applications.
A collection of must-read papers on recommendation systems and CTR prediction for developers working in computational advertising and AI-driven applications.
A TensorFlow implementation of a scalable distributed deep reinforcement learning framework.
This repository provides course materials for a reinforcement learning course, including lecture notes, tutorial tasks, and online videos.
An open-source post-building layer for AI agents, providing environment data and evaluations to power agent post-training and monitoring.
This repository provides a comprehensive guide and implementation for reinforcement learning algorithms in Python.
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