Finite sliding window policies achieve near-optimality and Q-learning converges to them for decentralized stochastic control under OSDISP and KSPISP information structures when a predictor stability condition holds in expected total variation.
Planning in Observable
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Approximations and Learning for Decentralized Stochastic Control and Near Optimal Finite Window Policies
Finite sliding window policies achieve near-optimality and Q-learning converges to them for decentralized stochastic control under OSDISP and KSPISP information structures when a predictor stability condition holds in expected total variation.
- Minimax-Optimal Policy Regret in Partially Observable Markov Games