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1 result for “UCRL-CMDP”

Learning in Markov Decision Processes under Constraintscs.LG

UCRL-CMDP algorithm optimizes RL with constraints on average costs.

problem Optimizing RL in MDPs with average cost constraints.

method Model-based RL algorithms maximizing reward while keeping costs within bounds.

result UCRL-CMDP algorithm's expected regret is upper-bounded by $T^{2\slash 3}$.

reinforcement learning Markov decision processes average cost constraints UCRL-CMDP regret analysis

2020-02-27abs ↗pdf ↗