arXiv research

A new learning method for prosthetic arms without explicit rewards.

Study on identifying most preferred policy in bandits with vector-valued rewards.

A new policy for contextual bandits adapts to reward vector shifts.

Model user preferences for conversational LLMs using weak rewards.

We consider reinforcement learning (RL) in Markov Decision Processes in which an agent repeatedly interacts with an environment that is modeled by a controlled Markov process. At each time step $t$, it earns a reward, and also incurs a cost-vector consisting of $M$ costs. We design model-based RL algorithms that maximi…

Method predicts future rewards from past actions in a linear Gaussian system.

In this paper, we introduce a novel combined reward cum penalty loss function to handle the regression problem. The proposed combined reward cum penalty loss function penalizes the data points which lie outside the $ε$-tube of the regressor and also assigns reward for the data points which lie inside of the $ε$-tube of…

Enhanced financial reward with shuffled feature CNN-DRL.

A contextual bandit problem is studied in a highly non-stationary environment, which is ubiquitous in various recommender systems due to the time-varying interests of users. Two models with disjoint and hybrid payoffs are considered to characterize the phenomenon that users' preferences towards different items vary dif…

Adaptive policies solve a linear program to maximize rewards while minimizing costs in sales with discounts.

In this paper, we study the stochastic version of the one-sided full information bandit problem, where we have $K$ arms $[K] = \{1, 2, \ldots, K\}$, and playing arm $i$ would gain reward from an unknown distribution for arm $i$ while obtaining reward feedback for all arms $j \ge i$. One-sided full information bandit ca…

New algorithm allows IGL to work with action-inclusive feedback.

New algorithm for fair ranking in contextual bandits with concave rewards.

Develops neural network framework for risk-reward optimization problems.

DPA aligns LLMs with multi-objective rewards for diverse user preferences.

Paper improves sample complexity for reward-free RL in low-rank MDPs.

New approach to multi-armed bandit problem aims to maximize highest total reward.

Study noisy rewards in online decision-making with unknown distributions.

The paper tackles combinatorial pure exploration with various feedback structures and proposes efficient algorithms.

Inverse reinforcement learning (IRL) is the problem of finding a reward function that generates a given optimal policy for a given Markov Decision Process. This paper looks at an algorithmic-independent geometric analysis of the IRL problem with finite states and actions. A L1-regularized Support Vector Machine formula…

In $\mathcal{X}$-armed bandit problem an agent sequentially interacts with environment which yields a reward based on the vector input the agent provides. The agent's goal is to maximise the sum of these rewards across some number of time steps. The problem and its variations have been a subject of numerous studies, su…

Thresholded Lasso bandit minimizes regret in sparse linear bandits.

New algorithms for efficient learning with long-term rewards in contextual bandits.

Optimal algorithm for maximizing rewards in contextual bandits with resource constraints.

Thompson sampling used for linear bandits with normal-gamma priors.

In a linear stochastic bandit model, each arm is a vector in an Euclidean space and the observed return at each time step is an unknown linear function of the chosen arm at that time step. In this paper, we investigate the problem of learning the best arm in a linear stochastic bandit model, where each arm's expected r…

Paper introduces G-LowTESTR for efficient tensor bandits.

This paper explores the complexity of learning representations in contextual linear bandits.

This paper proposes an exploration method for deep reinforcement learning based on parameter space noise. Recent studies have experimentally shown that parameter space noise results in better exploration than the commonly used action space noise. Previous methods devised a way to update the diagonal covariance matrix o…

We introduce a framework to study the effective objectives at different time scales of financial market microstructure. The financial market can be regarded as a complex adaptive system, where purposeful agents collectively and simultaneously create and perceive their environment as they interact with it. It has been s…

Motivated by drug design, we consider the best-arm identification problem in generalized linear bandits. More specifically, we assume each arm has a vector of covariates, there is an unknown vector of parameters that is common across the arms, and a generalized linear model captures the dependence of rewards on the cov…

It is notoriously difficult to control the behavior of reinforcement learning agents. Agents often learn to exploit the environment or reward signal and need to be retrained multiple times. The multi-objective reinforcement learning (MORL) framework separates a reward function into several objectives. An ideal MORL age…

We consider the linear contextual bandit problem with resource consumption, in addition to reward generation. In each round, the outcome of pulling an arm is a reward as well as a vector of resource consumptions. The expected values of these outcomes depend linearly on the context of that arm. The budget/capacity const…

A fair policy for hiring candidates from different groups is proposed in a linear contextual bandit problem.

Proposes EE-Net for neural exploration in contextual bandits.

Dual-structured method improves cross-domain imitation learning.

New method for identifying best designs in vector optimization with uncertain feedback.

The problem of multi-armed bandits (MAB) asks to make sequential decisions while balancing between exploitation and exploration, and have been successfully applied to a wide range of practical scenarios. Various algorithms have been designed to achieve a high reward in a long term. However, its short-term performance m…

Algorithm maximizes user rewards under per-item budget constraints.

In this paper, we study the multi-objective bandits (MOB) problem, where a learner repeatedly selects one arm to play and then receives a reward vector consisting of multiple objectives. MOB has found many real-world applications as varied as online recommendation and network routing. On the other hand, these applicati…

Algorithm identifies Pareto front using multiple context directions and reuses exploration samples.

We consider the multi-armed bandit setting with a twist. Rather than having just one decision maker deciding which arm to pull in each round, we have $n$ different decision makers (agents). In the simple stochastic setting, we show that a "free-riding" agent observing another "self-reliant" agent can achieve just $O(1)…

A new algorithm reduces communication costs for collaborative decision-making across clients.

Improved sample complexity for contextual combinatorial semi-bandits with sparse rewards.

Paper tackles non-monotonic resource utilization in sequential decision-making.

New algorithm resists corruption in linear contextual bandits.

A new algorithm learns from raw feature vectors using deep neural networks and UCB for exploration.

We study offline data poisoning attacks in contextual bandits, a class of reinforcement learning problems with important applications in online recommendation and adaptive medical treatment, among others. We provide a general attack framework based on convex optimization and show that by slightly manipulating rewards i…