gym-multigrid is a Python library extending OpenAI Gym with multi-room gridworld environments. It enables researchers to benchmark and develop reinforcement learning agents on navigation, exploration, and semantic tasks. Users can choose from predefined layouts or create custom grid maps with objects, doors, and locks. The package supports full or partial observability, flexible action spaces, and seamless integration with popular RL frameworks like Stable Baselines.
AI Platforms & Frameworks
Development Environment
Tool Libraries
#reinforcement learning
#gridworld environments
#navigation tasks
#AI benchmarking
#multi-room simulation
#customizable environments
#OpenAI Gym
#state representation
#action flexibility
#object interaction
#hierarchical planning
#Research Tools
#AI evaluation
#algorithm training
#exploration tasks
#lightweight dependencies
#modular design
#configurable layouts
#grid configurations
#object placement
...
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gym-multigrid
gym-multigrid is a Python library extending OpenAI Gym with multi-room gridworld environments. It enables researchers to benchmark and develop reinforcement learning agents on navigation, exploration, and semantic tasks. Users can choose from predefined layouts or create custom grid maps with objects, doors, and locks. The package supports full or partial observability, flexible action spaces, and seamless integration with popular RL frameworks like Stable Baselines.
AI Platforms & Frameworks
Development Environment
Tool Libraries
#reinforcement learning
#gridworld environments
#navigation tasks
#AI benchmarking
#multi-room simulation
#customizable environments
#OpenAI Gym
#state representation
#action flexibility
#object interaction
#hierarchical planning
#Research Tools
#AI evaluation
#algorithm training
#exploration tasks
#lightweight dependencies
#modular design
#configurable layouts
#grid configurations
#object placement
...
Featured
What is gym-multigrid?
gym-multigrid provides a suite of customizable gridworld environments designed for multi-room navigation and exploration tasks in reinforcement learning. Each environment consists of interconnected rooms populated with objects, keys, doors, and obstacles. Users can adjust grid size, room configurations, and object placements programmatically. The library supports both full and partial observation modes, offering RGB or matrix state representations. Actions include movement, object interaction, and door manipulation. By integrating it as a Gym environment, researchers can leverage any Gym-compatible agent, seamlessly training and evaluating algorithms on tasks like key-door puzzles, object retrieval, and hierarchical planning. gym-multigrid’s modular design and minimal dependencies make it ideal for benchmarking new AI strategies.
Who will use gym-multigrid?
- Reinforcement learning researchers
- AI developers experimenting with navigation tasks
- Academics teaching RL concepts
- Students learning Gym environments
How to use the gym-multigrid?
- Step1: Install gym-multigrid via pip: pip install gym-multigrid
- Step2: Import Gym and gym_multigrid: import gym, gym_multigrid
- Step3: Register or select an environment: env = gym.make('MiniGrid-MultiRoom-N2-v0')
- Step4: Initialize the environment: obs = env.reset()
- Step5: Execute actions in a loop: obs, reward, done, info = env.step(action)
- Step6: Render the environment: env.render()
- Step7: Close environment when finished: env.close()
Platform
- mac
- windows
- linux
gym-multigrid's Core Features & Benefits
The Core Features
- Multi-room gridworld environments
- Customizable layouts and object placement
- Full and partial observation spaces
- OpenAI Gym compatibility
- Flexible action and state representations
The Benefits
- Standardized benchmarking for navigation and exploration
- Easy integration with existing RL frameworks
- High configurability for research experiments
- Lightweight dependencies
- Open-source extensibility
gym-multigrid's Main Use Cases & Applications
- Benchmarking RL algorithms on multi-room navigation
- Researching hierarchical planning and exploration strategies
- Educational demos for reinforcement learning courses
- Developing key-door puzzle agents
FAQs of gym-multigrid
gym-multigrid Company Information
- Arnaud Fickinger
gym-multigrid Reviews
5/5
gym-multigrid's Main Competitors and alternatives?
- MiniGrid
- MazeBase
- Pycolab
- GridWorld
- ViZDoom
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