arXiv research

New algorithms for generalized linear bandits with unknown reward functions.

Consider a nonparametric contextual multi-arm bandit problem where each arm $a \in [K]$ is associated to a nonparametric reward function $f_a: [0,1] \to \mathbb{R}$ mapping from contexts to the expected reward. Suppose that there is a large set of arms, yet there is a simple but unknown structure amongst the arm reward…

Algorithm learns user's reward function from hypothetical behaviors.

CoCoRL learns safe constraints from demonstrations with unknown rewards.

In structured output prediction tasks, labeling ground-truth training output is often expensive. However, for many tasks, even when the true output is unknown, we can evaluate predictions using a scalar reward function, which may be easily assembled from human knowledge or non-differentiable pipelines. But searching th…

New algorithm learns safe policies in unknown environments.

Study reward-free RL in non-linear settings, improving efficiency and removing assumptions.

Paper proposes a method to learn and exceed expert demonstrations in unknown reward environments.

Optimal strategy proposed for maximizing cumulative reward in continuum-armed bandits.

PQR estimates reward functions from actions and states without assuming state-only rewards.

Inverse Reinforcement Learning (IRL) describes the problem of learning an unknown reward function of a Markov Decision Process (MDP) from observed behavior of an agent. Since the agent's behavior originates in its policy and MDP policies depend on both the stochastic system dynamics as well as the reward function, the …

This paper improves understanding of GAIL's generalization and computational efficiency.

B-REX efficiently learns Atari game policies from pixel inputs using Bayesian methods.

GACBO optimizes unknown causal graphs with interventions.

New algorithms improve best-arm identification with varying rewards.

New RL approach learns dynamic VCG mechanisms in unknown MDP environments.

New RL algorithm tackles adversarial RMAB with unknown transitions and bandit feedback.

Reinforcement learning agents are prone to undesired behaviors due to reward mis-specification. Finding a set of reward functions to properly guide agent behaviors is particularly challenging in multi-agent scenarios. Inverse reinforcement learning provides a framework to automatically acquire suitable reward functions…

Unified framework for estimating reward functions in competitive games.

We study the Combinatorial Pure Exploration problem with Continuous and Separable reward functions (CPE-CS) in the stochastic multi-armed bandit setting. In a CPE-CS instance, we are given several stochastic arms with unknown distributions, as well as a collection of possible decisions. Each decision has a reward accor…

New algorithm reduces regret for single-index bandits to nearly optimal.

Improved regret for single-index bandits with optimal algorithm.

New method for linear bandits with unknown sparsity, improving sparse regret bounds.

New method optimizes policies without assuming known link functions between preferences and rewards.

We consider the problem of learning to play a repeated multi-agent game with an unknown reward function. Single player online learning algorithms attain strong regret bounds when provided with full information feedback, which unfortunately is unavailable in many real-world scenarios. Bandit feedback alone, i.e., observ…

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…

MAXMINLCB optimizes unknown target functions with preference feedback using a Stackelberg game approach.

ORIL learns a reward function from unlabeled data to improve robot learning.

New algorithms reduce contextual bandits' regret without knowing reward noise variances.

The paper tackles resource allocation for arms with unknown and random rewards, achieving optimal regret bounds.

Reinforcement learning with trajectory feedback instead of state-action rewards.

Learning to make decisions from observed data in dynamic environments remains a problem of fundamental importance in a number of fields, from artificial intelligence and robotics, to medicine and finance. This paper concerns the problem of learning control policies for unknown linear dynamical systems so as to maximize…

Study resource allocation strategies in sequential decisions with unknown rewards.

Learning to imitate expert behavior from demonstrations can be challenging, especially in environments with high-dimensional, continuous observations and unknown dynamics. Supervised learning methods based on behavioral cloning (BC) suffer from distribution shift: because the agent greedily imitates demonstrated action…

We tackle the problem of acting in an unknown finite and discrete Markov Decision Process (MDP) for which the expected shortest path from any state to any other state is bounded by a finite number $D$. An MDP consists of $S$ states and $A$ possible actions per state. Upon choosing an action $a_t$ at state $s_t$, one re…

We study the stochastic contextual bandit problem, where the reward is generated from an unknown function with additive noise. No assumption is made about the reward function other than boundedness. We propose a new algorithm, NeuralUCB, which leverages the representation power of deep neural networks and uses a neural…

Develops statistical framework for resolving reward function ambiguity in inverse reinforcement learning.

Inverse optimal control, also known as inverse reinforcement learning, is the problem of recovering an unknown reward function in a Markov decision process from expert demonstrations of the optimal policy. We introduce a probabilistic inverse optimal control algorithm that scales gracefully with task dimensionality, an…

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

Modeling driver trajectories using inverse reinforcement learning and random utility.

Paper optimizes learning MDPs with adversarial rewards using linear models.

Adversarial RL recovers agent rewards from financial market data simulations.

Algorithm reduces regret in multi-player bandits with unknown collision rewards.

The paper develops a robust algorithm for contextual bandits with heavy-tailed rewards.

New IRL algorithm for continuous state spaces with formal guarantees.

We consider black box optimization of an unknown function in the nonparametric Gaussian process setting when the noise in the observed function values can be heavy tailed. This is in contrast to existing literature that typically assumes sub-Gaussian noise distributions for queries. Under the assumption that the unknow…

We consider a class of restless multi-armed bandit (RMAB) problems with unknown arm dynamics. At each time, a player chooses an arm out of N arms to play, referred to as an active arm, and receives a random reward from a finite set of reward states. The reward state of the active arm transits according to an unknown Ma…

We here adopt Bayesian nonparametric mixture models to extend multi-armed bandits in general, and Thompson sampling in particular, to scenarios where there is reward model uncertainty. In the stochastic multi-armed bandit, the reward for the played arm is generated from an unknown distribution. Reward uncertainty, i.e.…