Density-Matrix Reinforcement Learning
What information does a reinforcement learning policy omit?
ProofPaper ↗
Key points
- We store the policy at each state as a density matrix, a positive semidefinite, unit-trace matrix borrowed from quantum information theory.
- This gives density-matrix reinforcement learning (DMRL).
- First, suppose a policy is regularized toward a coherent reference policy using matrix relative entropy.
- In a two-action example, both policies assign probability $0.5$ to each action, yet the classical one earns expected reward $0$ while the coherent one earns $1$, the maximum.
Sources (1)
- [1]Density-Matrix Reinforcement LearningarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 06:00 PM
What information does a reinforcement learning policy omit?
We store the policy at each state as a density matrix, a positive semidefinite, unit-trace matrix borrowed from quantum information theory.
Extractive summary: sentences quoted from the sources.
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