Corpus ID: 231861555

Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent State

@article{Dong2021SimpleAC,
  title={Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent State},
  author={Shi Dong and Benjamin Van Roy and Zhengyuan Zhou},
  journal={ArXiv},
  year={2021},
  volume={abs/2102.05261}
}
We design a simple reinforcement learning (RL) agent that implements an optimistic version of Q-learning and establish through regret analysis that this agent can operate with some level of competence in any environment. While we leverage concepts from the literature on provably efficient RL, we consider a general agent-environment interface and provide a novel agent design and analysis. This level of generality positions our results to inform the design of future agents for operation in… Expand

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