Gym-Recsys is a Python-based framework offering OpenAI Gym-compatible environments designed to simulate user-item interactions. It enables researchers and engineers to train and benchmark reinforcement learning recommendation agents using synthetic or real-world datasets, with built-in user behavior models and standard evaluation metrics.
Promote this Tool
Update this Tool
Gym-Recsys
Gym-Recsys is a Python-based framework offering OpenAI Gym-compatible environments designed to simulate user-item interactions. It enables researchers and engineers to train and benchmark reinforcement learning recommendation agents using synthetic or real-world datasets, with built-in user behavior models and standard evaluation metrics.
Ads
What is Gym-Recsys?
Gym-Recsys is a toolbox that wraps recommendation tasks into OpenAI Gym environments, allowing reinforcement learning algorithms to interact with simulated user-item matrices step by step. It provides synthetic user behavior generators, supports loading popular datasets, and delivers standard recommendation metrics like Precision@K and NDCG. Users can customize reward functions, user models, and item pools to experiment with different RL-based recommendation strategies in a reproducible manner.
Who will use Gym-Recsys?
- Reinforcement learning researchers
- Recommender system engineers
- Data scientists in personalization
- Academic instructors in ML courses
How to use the Gym-Recsys?
- Step1: Install via pip install gym-recsys
- Step2: Import and load a built‐in or custom dataset
- Step3: Create an environment with gym.make('RecSys-v0')
- Step4: Define or plug in an RL agent (DQN, Policy Gradient, etc.)
- Step5: Train the agent by interacting with the environment
- Step6: Evaluate performance using provided metrics and logs
Platform
- Linux
- Mac
- Windows
Gym-Recsys's Core Features & Benefits
The Core Features
- OpenAI Gym-compatible recommendation environments
- Synthetic and real-world dataset support
- User behavior simulation modules
- Standard recommendation metrics integration
- Customizable reward and observation spaces
The Benefits
- Reproducible RL recommendation benchmarks
- Easy integration with common RL libraries
- Flexible environment configuration
- Scalable experiments on various data sizes
Gym-Recsys's Main Use Cases & Applications
- Developing and testing RL-based recommender algorithms
- Benchmarking recommendation strategies across datasets
- Teaching reinforcement learning concepts in personalization
- Simulating user engagement and item ranking dynamics
FAQs of Gym-Recsys
Gym-Recsys Company Information
- zuoxingdong
Gym-Recsys Reviews
5/5
Gym-Recsys's Main Competitors and alternatives?
Google RecSim
RecoGym
Microsoft Recommenders
IRKit
Spotlight
You may also like:
Run coding agents in a persistent cloud workspace with shared files, previews, team context, and no local setup required.
Diagrid keeps AI agent workflows running through crashes, preserves state, and cryptographically proves every completed execution step.
Run coding agents locally with swappable models, tools, sandboxes, and session logs through a TypeScript plugin runtime.
Loopa is an AI agent platform that automates research, content creation, analysis, and workflow execution.
An autonomous AI agent that executes long tasks across apps, websites, and cloud computers end to end.
Hosted OpenClaw agent: one-click deploy, 500+ models, secure infrastructure, and automated agent management for teams and developers.
Enterprise-ready agent runtime that unifies Discord, web, and terminal with secure RAG, memory, and tool execution.
OpenClaw is an open-source, locally-run personal AI assistant that automates tasks via chat apps and plugins.
Managed Openclaw workspace to deploy local-first AI agents, hire AI staff, and join the Moltbook ecosystem.
CoTester is an enterprise-grade AI testing agent that reliably generates, runs, and self-heals automated tests.
Conversational AI assistant automating research, browser tasks, web scraping, and file management through natural language.
Gobii lets teams create 24/7 autonomous digital workers to automate web research and routine tasks.
AI design agent automates workflow creating images, videos, 3D models up to 10x faster.
SJinn is an AI-powered agent creating image, video, audio, and 3D content from descriptions.
Eigent is an open-source AI workforce platform managing complex workflows via multi-agent collaboration.
NVIDIA Omniverse Audio2Face transforms 3D character animations with AI-driven facial and emotional expressions.