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Found 7 papers, 0 papers with code
A Unified Analysis of Nonstochastic Delayed Feedback for Combinatorial Semi-Bandits, Linear Bandits, and MDPs
We derive a new analysis of Follow The Regularized Leader (FTRL) for online learning with delayed bandit feedback.
Delay-Adapted Policy Optimization and Improved Regret for Adversarial MDP with Delayed Bandit Feedback
Policy Optimization (PO) is one of the most popular methods in Reinforcement Learning (RL).
Regret Minimization and Convergence to Equilibria in General-sum Markov Games
Our key observation is that online learning via policy optimization in Markov games essentially reduces to a form of weighted regret minimization, with unknown weights determined by the path length of the agents' policy sequence.
Near-Optimal Regret for Adversarial MDP with Delayed Bandit Feedback
The standard assumption in reinforcement learning (RL) is that agents observe feedback for their actions immediately.
Cooperative Online Learning in Stochastic and Adversarial MDPs
We study cooperative online learning in stochastic and adversarial Markov decision process (MDP).
Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions
We study the stochastic Multi-Armed Bandit (MAB) problem with random delays in the feedback received by the algorithm.
Learning Adversarial Markov Decision Processes with Delayed Feedback
We present novel algorithms based on policy optimization that achieve near-optimal high-probability regret of $\widetilde O ( \sqrt{K} + \sqrt{D} )$ under full-information feedback, where $K$ is the number of episodes and $D = \sum_{k} d^k$ is the total delay.
