Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism
We consider un-discounted reinforcement learning (RL) in Markov decision processes (MDPs) under drifting non-stationarity, i.e., both the reward and state transition distributions are allowed to evolve over time, as long as their respective total variations, quantified by suitable metrics, do not ex...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
2021-11-03T17:29:34Z.
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| Subjects: | |
| Online Access: | Get fulltext |