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Found 15 papers, 1 papers with code
PhysioZoo: The Open Digital Physiological Biomarkers Resource
PhysioZoo is a collaborative platform designed for the analysis of continuous physiological time series.
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).
Policy Optimization for Stochastic Shortest Path
Policy optimization is among the most popular and successful reinforcement learning algorithms, and there is increasing interest in understanding its theoretical guarantees.
Cooperative Online Learning in Stochastic and Adversarial MDPs
We study cooperative online learning in stochastic and adversarial Markov decision process (MDP).
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.
Planning and Learning with Adaptive Lookahead
Some of the most powerful reinforcement learning frameworks use planning for action selection.
Minimax Regret for Stochastic Shortest Path
In this work we show that the minimax regret for this setting is $\widetilde O(\sqrt{ (B_\star^2 + B_\star) |S| |A| K})$ where $B_\star$ is a bound on the expected cost of the optimal policy from any state, $S$ is the state space, and $A$ is the action space.
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.
Oracle-Efficient Regret Minimization in Factored MDPs with Unknown Structure
We study regret minimization in non-episodic factored Markov decision processes (FMDPs), where all existing algorithms make the strong assumption that the factored structure of the FMDP is known to the learner in advance.
Stochastic Shortest Path with Adversarially Changing Costs
Stochastic shortest path (SSP) is a well-known problem in planning and control, in which an agent has to reach a goal state in minimum total expected cost.
Near-optimal Regret Bounds for Stochastic Shortest Path
In this work we remove this dependence on the minimum cost---we give an algorithm that guarantees a regret bound of $\widetilde{O}(B_\star |S| \sqrt{|A| K})$, where $B_\star$ is an upper bound on the expected cost of the optimal policy, $S$ is the set of states, $A$ is the set of actions and $K$ is the number of episodes.
Optimistic Policy Optimization with Bandit Feedback
To the best of our knowledge, the two results are the first sub-linear regret bounds obtained for policy optimization algorithms with unknown transitions and bandit feedback.
Online Stochastic Shortest Path with Bandit Feedback and Unknown Transition Function
We consider online learning in episodic loop-free Markov decision processes (MDPs), where the loss function can change arbitrarily between episodes.
Online Convex Optimization in Adversarial Markov Decision Processes
We consider online learning in episodic loop-free Markov decision processes (MDPs), where the loss function can change arbitrarily between episodes, and the transition function is not known to the learner.
