Identifying Latent Actions and Dynamics from Offline Data via Demonstrator Diversity
Identifying Latent Actions and Dynamics from Offline Data via Demonstrator Diversity explores A method to recover latent actions and dynamics from offline trajectories using demonstrator diversity.. Commercial viability score: 4/10 in Reinforcement Learning.
Updated 2 days ago
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisToward Learning POMDPs Beyond Full-Rank Actions and State ObservabilityViability: 2.0
- Builds On ThisLearning Latent Action World Models In The WildViability: 2.0
- Higher ViabilityDiversity You Can Actually Measure: A Fast, Model-Free Diversity Metric for Robotics DatasetsViability: 7.0
- Higher ViabilityBeyond Mode Elicitation: Diversity-Preserving Reinforcement Learning via Latent Diffusion ReasonerViability: 7.0
- Higher ViabilityLocalized Dynamics-Aware Domain Adaption for Off-Dynamics Offline Reinforcement Learning
Related Resources
- Confidence-Calibrated Reinforcement Learning(glossary)
- Multi-Agent Reinforcement Learning(glossary)
- Maximum Entropy Reinforcement Learning(glossary)
- How does PRISM improve reinforcement learning?(question)
- What is the significance of reinforcement learning in AI?(question)
- How does RetroAgent improve reinforcement learning?(question)
- Reinforcement Learning – Use Cases(use_case)