eluder dimension
Reinforcement learning from Human Feedback (RLHF) learns from preference signals, while standard Reinforcement Learning (RL) directly learns from reward
The latter case can be further reduced to adversarial MDP when preferences only depend on the final state.
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A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation
As one of the most mainstream paradigms for sequential decision-making, RL has extensive applications in many real-world problems (Kober et al., 2013; Mnih et al.,
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Selective Sampling and Imitation Learning via Online Regression
We consider the problem of Imitation Learning (IL) by actively querying noisy expert for feedback.
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Experiment Planning with Function Approximation
We study the problem of experiment planning with function approximation in contextual bandit problems.
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