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Found 7 papers, 2 papers with code
A/B Testing and Best-arm Identification for Linear Bandits with Robustness to Non-stationarity
For robust identification, it is well-known that if arms are chosen randomly and non-adaptively from a G-optimal design over $\mathcal{X}$ at each time then the error probability decreases as $\exp(-T\Delta^2_{(1)}/d)$, where $\Delta_{(1)} = \min_{x \neq x^*} (x^* - x)^\top \frac{1}{T}\sum_{t=1}^T \theta_t$.
A Black-box Approach for Non-stationary Multi-agent Reinforcement Learning
Specifically, we focus on games with bandit feedback, where testing an equilibrium can result in substantial regret even when the gap to be tested is small, and the existence of multiple optimal solutions (equilibria) in stationary games poses extra challenges.
Offline congestion games: How feedback type affects data coverage requirement
Starting from the facility-level (a. k. a., semi-bandit) feedback, we propose a novel one-unit deviation coverage condition and give a pessimism-type algorithm that can recover an approximate NE.
Learning in Congestion Games with Bandit Feedback
We propose a centralized algorithm for Markov congestion games, whose sample complexity again has only polynomial dependence on all relevant problem parameters, but not the size of the action set.
Selective Sampling for Online Best-arm Identification
The main results of this work precisely characterize this trade-off between labeled samples and stopping time and provide an algorithm that nearly-optimally achieves the minimal label complexity given a desired stopping time.
Near-Optimal Randomized Exploration for Tabular Markov Decision Processes
To achieve the desired result, we develop 1) a new clipping operation to ensure both the probability of being optimistic and the probability of being pessimistic are lower bounded by a constant, and 2) a new recursive formula for the absolute value of estimation errors to analyze the regret.
Parameterized Indexed Value Function for Efficient Exploration in Reinforcement Learning
We use an indexed value function to represent uncertainty in our action-value estimates.
