Sample Complexity Bounds of Exploration

21 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Reinforcement Learning in Healthcare: A Survey (2019, background): “Other fundamental issues in RL research include but are not limited to the credit assignment problem [14], [27], the sampel/space/time complexity [28], [29], function approximation [30], [31], safety [32], [33], robustness [34], [35], and”
RLPy: a value-function-based reinforcement learning framework for education and research (2015, methodology): “How. powerful framework and aim at improving existing methods by, for example, feature learning (Keller et al., 2006; Parr et al., 2007; Geramifard et al., 2011), or policies with better exploration-exploitation trade-off (Nouri and Littman, 2009; Jaksch et al., 2010; Li, 2012).”
Reward-Free Exploration for Reinforcement Learning (2020, background): “Here, simple randomized exploration strategies provably fail: for example, a random walk can take exponential time to reach the corner of the environment where the agent can accummulate high reward (Li, 2012).”
Bounded Optimal Exploration in MDP (2016, methodology): “In practice, however, heuristics are still commonly used (Li 2012).”