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
Share This
💡 Learn minimum action distance (MAD) in MDPs without rewards or actions!
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