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Found 4 papers, 0 papers with code
RLHF from Heterogeneous Feedback via Personalization and Preference Aggregation
We propose two approaches based on reward and preference aggregation, respectively: the former utilizes both utilitarianism and Leximin approaches to aggregate individual reward models, with sample complexity guarantees; the latter directly aggregates the human feedback in the form of probabilistic opinions.
Provably Feedback-Efficient Reinforcement Learning via Active Reward Learning
We provide an active-learning-based RL algorithm that first explores the environment without specifying a reward function and then asks a human teacher for only a few queries about the rewards of a task at some state-action pairs.
Learning Rationalizable Equilibria in Multiplayer Games
This paper develops the first line of efficient algorithms for learning rationalizable Coarse Correlated Equilibria (CCE) and Correlated Equilibria (CE) whose sample complexities are polynomial in all problem parameters including the number of players.
Online Sub-Sampling for Reinforcement Learning with General Function Approximation
For a value-based method with complexity-bounded function class, we show that the policy only needs to be updated for $\propto\operatorname{poly}\log(K)$ times for running the RL algorithm for $K$ episodes while still achieving a small near-optimal regret bound.
