Reinforcement Learning
Showing 36 of 167 projects
A TensorFlow library for implementing, deploying, and testing Contextual Bandits and Reinforcement Learning algorithms.
A JAX-native library implementing Monte Carlo tree search algorithms like AlphaZero and MuZero for reinforcement learning research.
A modular, high-throughput PyTorch framework for deep reinforcement learning research, supporting policy gradient, deep Q-learning, and Q-function policy gradient algorithms.
PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control.
Open-source robot simulation software integrated with OpenAI Gym for reinforcement learning research.
A toolkit and library for developing, evaluating, and reproducing reinforcement learning algorithms.
A platform for developing AI bots that play Doom using visual information, designed for reinforcement learning research.
A Python toolkit for training reinforcement learning agents and backtesting rule-based algorithms in financial markets.
An open-source benchmark suite of continuous control robotic manipulation environments for multi-task and meta reinforcement learning.
A deep reinforcement learning framework for financial portfolio management with policy gradient optimization and backtesting tools.
Original DeepMind DQN 3.0 implementation for Atari game reinforcement learning, with community tweaks.
An open-source, near photo-realistic 3D simulation platform for training and evaluating embodied AI agents.
Clean PyTorch implementations of imitation and reward learning algorithms for reinforcement learning.
An experimentation platform for training and researching automated agents in abstract simulated enterprise network environments using reinforcement learning.
A lightweight library providing PyTorch training tools and utilities to simplify and standardize training loops.
A C++-based high-performance parallel environment execution engine for vectorized reinforcement learning simulations.
A curated list of awesome links, software libraries, and resources for robotics development.
A JAX-based library providing reinforcement learning building blocks for implementing agents, supporting both on-policy and off-policy learning.
A flexible modular quadrotor simulator with decoupled rendering (Unity) and physics engines for robotics research.
A repository implementing Deep Reinforcement Learning and Supervised Learning methods with a simulated financial market environment for quantitative trading.
A modular deep reinforcement learning framework in PyTorch for research and application, featuring ready-to-use algorithms and reproducible experiments.
A computational framework for deep reinforcement learning experiments in traffic microsimulation and control.
High-performance, end-to-end reinforcement learning implementations fully written in JAX for massive parallelization on GPUs.
OpenAI Gym environments and tools for training a 3D-printed quadruped robot (SpotMicro) using reinforcement learning.
A modular Python library for Reinforcement Learning with support for PyTorch, JAX, NVIDIA Warp, and multiple environment interfaces.
A high-performance, portable deep reinforcement learning library for continuous control, optimized for speed across CPUs, GPUs, and microcontrollers.
A scalable, event-driven backtesting library for reinforcement learning in algorithmic trading, built on Backtrader with OpenAI Gym API.
A flexible Python framework for developing, training, and evaluating conversational AI agents in single or multi-agent environments.
A scalable, hardware-accelerated neuroevolution toolkit built on JAX for parallel training across TPUs/GPUs.
A curated collection of research papers on molecular and material design using generative AI and deep learning techniques.
A virtual environment simulator for training embodied AI agents with real-world perception and physics, featuring domain transfer to real robots.
An API conversion tool providing Gymnasium and PettingZoo bindings for popular external reinforcement learning environments.
A JAX-based library providing accelerated reinforcement learning environments with full compatibility to the classic gym API.
A unified benchmark suite with PyBullet CartPole and Quadrotor environments and symbolic CasADi dynamics for safe learning-based control and reinforcement learning.
An open-source simulator for experimenting with and advancing self-driving AI, accessible to anyone with a PC.
A CPU and GPU-accelerated machine learning library optimized for high-performance computing.
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