Learning The Minimum Action Distance
📰 ArXiv cs.AI
Learning the minimum action distance (MAD) in Markov decision processes (MDPs) from state trajectories without reward signals or actions
Action Steps
- Learn the minimum action distance (MAD) from state trajectories
- Use MAD as a metric to capture the underlying structure of an environment
- Apply the learned state representation to improve decision-making in MDPs
- Evaluate the effectiveness of the MAD framework in various environments
Who Needs to Know This
This research benefits AI engineers and ML researchers working on reinforcement learning and MDPs, as it provides a new framework for learning state representations
Key Insight
💡 MAD can be learned solely from state trajectories and captures the underlying structure of an environment
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💡 Learn minimum action distance (MAD) in MDPs without rewards or actions!
Key Takeaways
Learning the minimum action distance (MAD) in Markov decision processes (MDPs) from state trajectories without reward signals or actions
Full Article
Title: Learning The Minimum Action Distance
Abstract:
arXiv:2506.09276v3 Announce Type: replace-cross Abstract: This paper presents a state representation framework for Markov decision processes (MDPs) that can be learned solely from state trajectories, requiring neither reward signals nor the actions executed by the agent. We propose learning the minimum action distance (MAD), defined as the minimum number of actions required to transition between states, as a fundamental metric that captures the underlying structure of an environment. MAD natural
Abstract:
arXiv:2506.09276v3 Announce Type: replace-cross Abstract: This paper presents a state representation framework for Markov decision processes (MDPs) that can be learned solely from state trajectories, requiring neither reward signals nor the actions executed by the agent. We propose learning the minimum action distance (MAD), defined as the minimum number of actions required to transition between states, as a fundamental metric that captures the underlying structure of an environment. MAD natural
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