Online Reinforcement Learning in Factored Markov Decision Processes and Unknown Markov Games
Reinforcement learning (RL) has gained an increasing interest in recent years, being expected to deliver autonomous agents that can learn to interact with an environment. So far the empirical successes rely heavily on enormous amount of data collected during interaction, hence mostly limited to doma...
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/139468 |