Deep Reinforcement Learning
Showing 36 of 39 projects
Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility and transparency.
Open-source simulator for drones and autonomous vehicles built on Unreal Engine and Unity, designed for AI research.
An open-source framework for applying deep reinforcement learning to quantitative finance, featuring a train-test-trade pipeline for stock and crypto trading.
An open-source simulator built on Unreal Engine for developing, training, and validating autonomous driving systems.
A deep reinforcement learning library offering high-quality, single-file implementations of algorithms like PPO, DQN, and SAC for research and education.
A curated list of resources dedicated to reinforcement learning, including theory, applications, code, tutorials, and platforms.
A curated list of reinforcement learning resources including theory, applications, code libraries, tutorials, and platforms.
An AI-powered WiFi security auditing tool that uses deep reinforcement learning to optimize capture of WPA handshakes.
An end-to-end deep learning library focused on clear code and speed, used for research and production by Google Brain.
An end-to-end deep learning library focused on clear code, speed, and research, built by Google Brain.
An open course on reinforcement learning with a practical focus, featuring hands-on labs and comprehensive materials for both online and on-campus students.
An open course on reinforcement learning with a practical focus, featuring hands-on labs and comprehensive materials for both online and on-campus students.
A curated list of awesome resources for applying LLMs and deep learning to financial market analysis and algorithmic trading.
A Python library implementing state-of-the-art deep reinforcement learning algorithms with seamless Keras integration.
A modular TensorFlow library for applied reinforcement learning with a focus on flexible design and practical usability.
A modular TensorFlow library for applied reinforcement learning with a focus on flexible design and practical usability.
An AI-native modular infrastructure for quantitative trading, featuring a weight-centric architecture for building, testing, and deploying algorithmic strategies.
TensorFlow implementation of Deep Q-Networks (DQN) for human-level control in reinforcement learning environments.
A modular, high-throughput PyTorch framework for deep reinforcement learning research, supporting policy gradient, deep Q-learning, and Q-function policy gradient algorithms.
A Python library for offline deep reinforcement learning with support for state-of-the-art algorithms and user-friendly APIs.
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.
High-performance, end-to-end reinforcement learning implementations fully written in JAX for massive parallelization on GPUs.
A scalable, event-driven backtesting library for reinforcement learning in algorithmic trading, built on Backtrader with OpenAI Gym API.
A high-performance, portable deep reinforcement learning library for continuous control, optimized for speed across CPUs, GPUs, and microcontrollers.
A virtual environment simulator for training embodied AI agents with real-world perception and physics, featuring domain transfer to real robots.
An open-source simulator for experimenting with and advancing self-driving AI, accessible to anyone with a PC.
An OpenAI Gym extension for robotics simulation using ROS and Gazebo to benchmark and develop robot behaviors.
JAX (Flax) implementations of reinforcement learning algorithms for continuous action spaces, designed for research.
A deep reinforcement learning framework for crowd-aware robot navigation using attention mechanisms to model human-robot and human-human interactions.
ROS package implementing a deep reinforcement learning algorithm for dynamic obstacle avoidance in ground robots.
A modular deep reinforcement learning framework for portfolio management, enabling algorithmic stock trading with DQN and DDPG agents.
An open-source implementation reproducing DeepMind's Atari-playing deep reinforcement learning system from their seminal 2013 paper.
An OpenAI Gym environment wrapper for the CARLA autonomous driving simulator, enabling reinforcement learning research.
An application-oriented Deep Reinforcement Learning framework for real-world decision problems, covering simulation to deployment.
A Deep Q-learning reinforcement learning agent for automated stock trading using historical market data.
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