arXiv research

DBQPG improves policy gradient estimation with fewer samples.

Paper proposes MCTSPO for better reinforcement learning policy optimization.

New algorithm reduces variance in Monte Carlo simulations using deep neural networks and policy gradients.

The paper proposes a new method to estimate optimal policies using MCMC.

GPG improves RL from feedback with less memory and compute.

In this paper, we present an online reinforcement learning algorithm, called Renewal Monte Carlo (RMC), for infinite horizon Markov decision processes with a designated start state. RMC is a Monte Carlo algorithm and retains the advantages of Monte Carlo methods including low bias, simplicity, and ease of implementatio…

This paper introduces a set of algorithms for Monte-Carlo Bayesian reinforcement learning. Firstly, Monte-Carlo estimation of upper bounds on the Bayes-optimal value function is employed to construct an optimistic policy. Secondly, gradient-based algorithms for approximate upper and lower bounds are introduced. Finally…

New adaptive temperature selection improves parallel tempering efficiency.

Monte Carlo Tree Search (MCTS) algorithms perform simulation-based search to improve policies online. During search, the simulation policy is adapted to explore the most promising lines of play. MCTS has been used by state-of-the-art programs for many problems, however a disadvantage to MCTS is that it estimates the va…

New method optimizes decision-making in uncertain environments.

A method for accurate pricing of multidimensional derivatives under uncertain volatility.

Novel approach to Bayesian experimental design for non-exchangeable data.

PBVFs generalize across policies using learned value functions.

Deep hedging uses RL to minimize risk in financial markets.

Paper speeds up policy optimization for large recommendation systems.

Adaptive correlated MC improves sequence generation stability.

Policy gradient methods have demonstrated success in reinforcement learning tasks that have high-dimensional continuous state and action spaces. However, policy gradient methods are also notoriously sample inefficient. This can be attributed, at least in part, to the high variance in estimating the gradient of the task…

This paper compares eight DRL algorithms for dynamic hedging.

New algorithms improve Langevin Monte Carlo efficiency.

Gradient-based Monte Carlo sampling algorithms, like Langevin dynamics and Hamiltonian Monte Carlo, are important methods for Bayesian inference. In large-scale settings, full-gradients are not affordable and thus stochastic gradients evaluated on mini-batches are used as a replacement. In order to reduce the high vari…

Paper shows MCTS approximates policy optimization, proposing an improved variant.

Policy analysts wish to visualize a range of policies for large simulator-defined Markov Decision Processes (MDPs). One visualization approach is to invoke the simulator to generate on-policy trajectories and then visualize those trajectories. When the simulator is expensive, this is not practical, and some method is r…

This work overcomes bias in concave multi-objective reinforcement learning.

New method corrects bias in stochastic gradient samplers.

Policy gradient methods are powerful reinforcement learning algorithms and have been demonstrated to solve many complex tasks. However, these methods are also data-inefficient, afflicted with high variance gradient estimates, and frequently get stuck in local optima. This work addresses these weaknesses by combining re…

Many machine learning problems involve Monte Carlo gradient estimators. As a prominent example, we focus on Monte Carlo variational inference (MCVI) in this paper. The performance of MCVI crucially depends on the variance of its stochastic gradients. We propose variance reduction by means of Quasi-Monte Carlo (QMC) sam…

Proposes a method to reduce parallel complexity of MLMC in SGD.

New method combines neural networks with Monte Carlo for complex system reliability.

We propose kernel sequential Monte Carlo (KSMC), a framework for sampling from static target densities. KSMC is a family of sequential Monte Carlo algorithms that are based on building emulator models of the current particle system in a reproducing kernel Hilbert space. We here focus on modelling nonlinear covariance s…

Hamiltonian Monte Carlo (HMC) is a popular Markov chain Monte Carlo (MCMC) algorithm that generates proposals for a Metropolis-Hastings algorithm by simulating the dynamics of a Hamiltonian system. However, HMC is sensitive to large time discretizations and performs poorly if there is a mismatch between the spatial geo…

Paper proposes a new method for sampling from complex distributions.

Interpolates between SPG and NeuRD with Capped Implicit Exploration.

New algorithm reduces overfitting in neural networks.

Bayesian method infers contextual bandit policies robustly.

New method samples from piecewise smooth distributions using Hamiltonian Monte Carlo.

We propose Kernel Hamiltonian Monte Carlo (KMC), a gradient-free adaptive MCMC algorithm based on Hamiltonian Monte Carlo (HMC). On target densities where classical HMC is not an option due to intractable gradients, KMC adaptively learns the target's gradient structure by fitting an exponential family model in a Reprod…

Gradient learning optimises MCMC proposal distributions.

Unbiased method for Bayesian posterior means using kinetic Langevin dynamics.

New gradient estimator improves training for normalizing flows.

New methods improve efficiency of sampling algorithms for complex systems.

The paper proposes a new method to approximate Wasserstein-Fisher-Rao flows using Monte Carlo techniques.

New algorithms improve sampling from Bayesian deep learning models.

A new tamed stochastic gradient Hamiltonian Monte Carlo algorithm for superlinearly growing stochastic gradients.

This paper is a broad and accessible survey of the methods we have at our disposal for Monte Carlo gradient estimation in machine learning and across the statistical sciences: the problem of computing the gradient of an expectation of a function with respect to parameters defining the distribution that is integrated; t…

Stochastic Gradient Hamiltonian Monte Carlo (SGHMC) is a momentum version of stochastic gradient descent with properly injected Gaussian noise to find a global minimum. In this paper, non-asymptotic convergence analysis of SGHMC is given in the context of non-convex optimization, where subsampling techniques are used o…

Markov chain Monte Carlo (MCMC) algorithms are generally regarded as the gold standard technique for Bayesian inference. They are theoretically well-understood and conceptually simple to apply in practice. The drawback of MCMC is that in general performing exact inference requires all of the data to be processed at eac…

A new sampling method, RC-LMC, reduces computational cost for high-dimensional log-concave distributions.

New method tunes SMC samplers efficiently without high costs.