Search Results for author:
Found 20 papers, 5 papers with code
Stable Matching with Ties: Approximation Ratios and Learning
We aim to guarantee each worker the largest possible share from the utility in her best possible stable matching.
Improved Algorithms for Contextual Dynamic Pricing
For this model, our algorithm obtains a regret $\tilde{\mathcal{O}}(T^{d+2\beta/d+3\beta})$, where $d$ is the dimension of the context space.
Reinforcement Learning with Lookahead Information
We study reinforcement learning (RL) problems in which agents observe the reward or transition realizations at their current state before deciding which action to take.
On Bits and Bandits: Quantifying the Regret-Information Trade-off
We study the trade-off between the information an agent accumulates and the regret it suffers.
The Value of Reward Lookahead in Reinforcement Learning
In particular, we measure the ratio between the value of standard RL agents and that of agents with partial future-reward lookahead.
Ranking with Popularity Bias: User Welfare under Self-Amplification Dynamics
We highlight the importance of exploration, not to eliminate popularity bias, but to mitigate its negative impact on welfare.
Reinforcement Learning with History-Dependent Dynamic Contexts
We introduce Dynamic Contextual Markov Decision Processes (DCMDPs), a novel reinforcement learning framework for history-dependent environments that generalizes the contextual MDP framework to handle non-Markov environments, where contexts change over time.
On Preemption and Learning in Stochastic Scheduling
We study single-machine scheduling of jobs, each belonging to a job type that determines its duration distribution.
Reinforcement Learning with a Terminator
We learn the parameters of the TerMDP and leverage the structure of the estimation problem to provide state-wise confidence bounds.
Query-Reward Tradeoffs in Multi-Armed Bandits
We consider a stochastic multi-armed bandit setting where reward must be actively queried for it to be observed.
Ensemble Bootstrapping for Q-Learning
Finally, We demonstrate the superior performance of a deep RL variant of EBQL over other deep QL algorithms for a suite of ATARI games.
Confidence-Budget Matching for Sequential Budgeted Learning
We analyze the performance of CBM based algorithms in different settings and show that they perform well in the presence of adversity in the contexts, initial states, and budgets.
Reinforcement Learning with Trajectory Feedback
The standard feedback model of reinforcement learning requires revealing the reward of every visited state-action pair.
Lenient Regret for Multi-Armed Bandits
Importantly, we show that when the mean of the optimal arm is high enough, the lenient regret of $\epsilon$-TS is bounded by a constant.
Tight Lower Bounds for Combinatorial Multi-Armed Bandits
The Combinatorial Multi-Armed Bandit problem is a sequential decision-making problem in which an agent selects a set of arms on each round, observes feedback for each of these arms and aims to maximize a known reward function of the arms it chose.
Never Worse, Mostly Better: Stable Policy Improvement in Deep Reinforcement Learning
In recent years, there has been significant progress in applying deep reinforcement learning (RL) for solving challenging problems across a wide variety of domains.
Stabilizing Off-Policy Reinforcement Learning with Conservative Policy Gradients
In recent years, advances in deep learning have enabled the application of reinforcement learning algorithms in complex domains.
Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies
In this paper, we focus on model-based RL in the finite-state finite-horizon MDP setting and establish that exploring with \emph{greedy policies} -- act by \emph{1-step planning} -- can achieve tight minimax performance in terms of regret, $\tilde{\mathcal{O}}(\sqrt{HSAT})$.
Model-based Reinforcement Learning
reinforcement-learning
+2
Batch-Size Independent Regret Bounds for the Combinatorial Multi-Armed Bandit Problem
We show that a linear dependence of the regret in the batch size in existing algorithms can be replaced by this smoothness parameter.
Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning
Learning how to act when there are many available actions in each state is a challenging task for Reinforcement Learning (RL) agents, especially when many of the actions are redundant or irrelevant.
