SMOSE

SMOSE is a novel method for training interpretable controllers in reinforcement learning for continuous control tasks. It uses a Sparse Mixture of Shallow Experts architecture to combine interpretable decision-makers and a router for task assignment. SMOSE aims to improve transparency and trust in automated systems by providing interpretable policies.

Category: Artificial Intelligence
Subcategory: Reinforcement Learning
Tags: reinforcement learninginterpretable policiescontinuous controlsparse mixturedecision-making
AI Type: Reinforcement Learning
Programming Languages: Not specified
Frameworks/Libraries: Not specified
Application Areas: Automated systemsroboticsdecision-making
Manufacturer Company: Not specified
Country: Not specified
Algorithms Used

Sparse Mixture of Shallow Experts, decision trees

Model Architecture

Mixture-of-Experts architecture

Datasets Used

MuJoCo benchmark environments

Performance Metrics

Outperforms interpretable baselines

Deployment Options

Not specified

Cloud Based

No

On Premises

Yes

Features

Interpretable controllers, sparse mixture architecture, improved transparency

Enterprise

Yes

Hardware Requirements

Not specified

Supported Platforms

Not specified

Interoperability

Not specified

Security Features

Not specified

Compliance Standards

Not specified

Certifications

Not specified

Open Source

No

Source Code URL

http://Not specified

Documentation URL

http://Not specified

Community Support

Not specified

Contributors

Not specified

Training Data Size

Not specified

Inference Latency

Not specified

Energy Efficiency

Not specified

Explainability Features

Interpretable decision-making

Ethical Considerations

Not specified

Known Limitations

Not specified

Industry Verticals

Robotics, Automation

Use Cases

Interpretable reinforcement learning, automated decision-making

Customer Base

Not specified

Integration Options

Not specified

Scalability

High

Support Options

Not specified

SLA

Not specified

User Interface

Not specified

Multi-Language Support

No

Localization

Not specified

Pricing Model

Not specified

Trial Availability

No

Partner Ecosystem

Not specified

Patent Information

Not specified

Regulatory Compliance

Not specified

Version

Not specified

Website URL

http://Not specified

Service Type

Not specified

Has API

No

API Details

Not specified

Business Model

Not specified

Price

0.00

Currency

Not specified

License Type

Not specified

Release Date

01/01/1970

Last Update Date

01/01/1970

Contact Email

Not specified

Contact Phone

Not specified

Social Media Links

http://Not specified

Other Features

Not specified

Published

Yes