Joint Learning of Hierarchical Neural Options and Abstract World Model
Joint Learning of Hierarchical Neural Options and Abstract World Model explores AgentOWL enhances AI skill composition efficiency in Object-Centric Atari games through hierarchical learning.. Commercial viability score: 3/10 in Agents.
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 $10K - $14K 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 ThisBoosting deep Reinforcement Learning using pretraining with Logical OptionsViability: 2.0
- Higher ViabilityAgent World Model: Infinity Synthetic Environments for Agentic Reinforcement LearningViability: 7.0
- Higher ViabilityARISE: Agent Reasoning with Intrinsic Skill Evolution in Hierarchical Reinforcement LearningViability: 9.0
- Higher ViabilityAutomating Skill Acquisition through Large-Scale Mining of Open-Source Agentic Repositories: A Framework for Multi-Agent Procedural Knowledge ExtractionViability: 7.0
- Higher Viability
Related Resources
- AgentSpeak(glossary)
- Mixture-of-Agents(glossary)
- Agents(glossary)
- What is the future of AI agents according to Nothing's CEO?(question)
- How do LLM efficiency advancements impact the development of AI agents?(question)
- How does AgentXRay contribute to the explainability of AI agents in complex decision-making processes?(question)
- Agents – Use Cases(use_case)