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Exploration-Exploitation in Constrained MDPs
In many sequential decision-making problems, the goal is to optimize a u...
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Reinforcement Learning of Markov Decision Processes with Peak Constraints
In this paper, we consider reinforcement learning of Markov Decision Pro...
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Improved Algorithms for Conservative Exploration in Bandits
In many fields such as digital marketing, healthcare, finance, and robot...
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Stochastic Primal-Dual Methods and Sample Complexity of Reinforcement Learning
We study the online estimation of the optimal policy of a Markov decisio...
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Receding Horizon Curiosity
Sample-efficient exploration is crucial not only for discovering rewardi...
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Exploration-Exploitation Trade-off in Reinforcement Learning on Online Markov Decision Processes with Global Concave Rewards
We consider an agent who is involved in a Markov decision process and re...
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Learning Diagnostic Policies from Examples by Systematic Search
A diagnostic policy specifies what test to perform next, based on the re...
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Conservative Exploration in Reinforcement Learning
While learning in an unknown Markov Decision Process (MDP), an agent should trade off exploration to discover new information about the MDP, and exploitation of the current knowledge to maximize the reward. Although the agent will eventually learn a good or optimal policy, there is no guarantee on the quality of the intermediate policies. This lack of control is undesired in real-world applications where a minimum requirement is that the executed policies are guaranteed to perform at least as well as an existing baseline. In this paper, we introduce the notion of conservative exploration for average reward and finite horizon problems. We present two optimistic algorithms that guarantee (w.h.p.) that the conservative constraint is never violated during learning. We derive regret bounds showing that being conservative does not hinder the learning ability of these algorithms.
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