A new paper published on arXiv demonstrates that Howard's policy iteration algorithm can exhibit exponential iteration lower bounds for deterministic Markov decision processes, even with a limited number of actions. This finding establishes that the algorithm is not strongly polynomial when the discount factor is part of the input. The research highlights a significant gap between decentralized, selfish improvements and coordinated action selection, illustrating a "price" of algorithmic anarchy. AI
IMPACT Highlights theoretical limitations in decision-making algorithms, potentially impacting future research in reinforcement learning.
RANK_REASON Academic paper published on arXiv detailing theoretical limitations of an algorithm. [lever_c_demoted from research: ic=1 ai=1.0]
- algorithmic anarchy
- arXiv
- Dantzig's pivoting rule
- deterministic Markov decision processes
- Howard's policy iteration
- simplex algorithm
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