Showing 221-240 of 323 projects
This project uses reinforcement learning to train an AI agent to learn stock trading strategies.
This TensorFlow code implements a curiosity-driven exploration algorithm for deep reinforcement learning.
A reinforcement learning-based approach to scale training of variational autoencoders (VLAs) used in AI tools.
A curated list of best AI resources for developers, including tutorials, libraries, and tools.
Concise PyTorch implementations of popular deep reinforcement learning algorithms like REINFORCE, A2C, DQN, PPO, DDPG, TD3, and SAC.
A JavaScript library for building reinforcement learning agents, covering various RL algorithms.
Reference Jupyter notebooks and demo AI/ML applications for enterprise use cases
A collection of concise and beautiful algorithms written in the Julia programming language.
A Python library for reinforcement learning, providing a simple and efficient way to implement RL algorithms.
Softlearning is a powerful reinforcement learning framework for training maximum entropy policies in continuous domains.
A comprehensive implementation of all reinforcement learning algorithms in a simpler way.
Personae is a repo for implementing and experimenting with Deep Reinforcement Learning and Supervised Learning for Quantitative Trading.
TextWorld is a sandbox learning environment for training and evaluating reinforcement learning agents on text-based games.
TorchCraft is a library that connects the Torch deep learning framework to the StarCraft game engine, enabling AI research and development.
A scalable toolkit for efficient model reinforcement learning in Python.
An open-source package that allows game creators, AI researchers and hobbyists to build complex behaviors for non-player characters or agents.
ProtoMotions is a GPU-accelerated simulation and learning framework for training physically simulated digital humans and humanoid robots.
A high-performance, AI-powered Riichi Mahjong engine written in Rust for developers interested in game AI.
A gym for reinforcement learning with verifiable rewards, focusing on reasoning and language models.
A header-only C++ and CUDA physics library for reinforcement learning and robotics with zero dependencies.
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