Provably Efficient Primal-Dual Reinforcement Learning for CMDPs with Non-stationary Objectives and Constraints. (arXiv:2201.11965v1 [cs.LG])

Web: http://arxiv.org/abs/2201.11965

We consider primal-dual-based reinforcement learning (RL) in episodic
constrained Markov decision processes (CMDPs) with non-stationary objectives
and constraints, which play a central role in ensuring the safety of RL in
time-varying environments. In this problem, the reward/utility functions and
the state transition functions are both allowed to vary arbitrarily over time
as long as their cumulative variations do not exceed certain known variation
budgets. Designing safe RL algorithms in time-varying environments is
particularly challenging because of the need to integrate …

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