A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study compares model-free valuation to actual financial outcomes, finds it slightly conservative.
New model-free DR-RL algorithm with finite sample complexity.
Improved model-free RL from images with stable training.
New model-free algorithm achieves similar LQR regret guarantees.
Integrating model-free and model-based approaches in reinforcement learning has the potential to achieve the high performance of model-free algorithms with low sample complexity. However, this is difficult because an imperfect dynamics model can degrade the performance of the learning algorithm, and in sufficiently com…
Unified reinforcement learning methods using hybrid inference.
Q-Ensembles are a model-free approach where input images are fed into different Q-networks and exploration is driven by the assumption that uncertainty is proportional to the variance of the output Q-values obtained. They have been shown to perform relatively well compared to other exploration strategies. Further, mode…
Develops a new model-free approach to portfolio theory using rough paths.
Simple image augmentation boosts deep RL from pixels.
Framework uses probabilistic programming for physics simulation in games.
RandQL is a new model-free algorithm for MDPs with a novel learning rate randomization approach.
This paper compares model-based and model-free control methods using neural networks.
The paper presents a model-free method for stabilizing unknown control systems.
Recent success in deep reinforcement learning for continuous control has been dominated by model-free approaches which, unlike model-based approaches, do not suffer from representational limitations in making assumptions about the world dynamics and model errors inevitable in complex domains. However, they require a lo…
In this paper we introduce a new approach to model-free path-dependent option pricing. We first introduce a general duality result for linear optimisation problems over signed measures introduced in [3] and show how the the problem of model-free option pricing can be formulated in the new framework. We then introduce a…
New hybrid RL algorithm outperforms model-free and model-based methods.
Paper shows intrinsic motivation boosts exploration efficiency in HRL.
We study the sample complexity of model-based reinforcement learning (henceforth RL) in general contextual decision processes that require strategic exploration to find a near-optimal policy. We design new algorithms for RL with a generic model class and analyze their statistical properties. Our algorithms have sample …
Deep learning for financial derivatives pricing and hedging.
Improves sample efficiency in RL by matching model-based gradients.
Model-free approaches for reinforcement learning (RL) and continuous control find policies based only on past states and rewards, without fitting a model of the system dynamics. They are appealing as they are general purpose and easy to implement; however, they also come with fewer theoretical guarantees than model-bas…
Incorporating computational fluid dynamics in the design process of jets, spacecraft, or gas turbine engines is often challenged by the required computational resources and simulation time, which depend on the chosen physics-based computational models and grid resolutions. An ongoing problem in the field is how to simu…
Unified Latent Dynamics unifies model-free and model-based reinforcement learning.
Policy Prediction Network improves continuous control problems with model-free and model-based learning.
Model-free reinforcement learning (RL) algorithms, such as Q-learning, directly parameterize and update value functions or policies without explicitly modeling the environment. They are typically simpler, more flexible to use, and thus more prevalent in modern deep RL than model-based approaches. However, empirical wor…
The goal of reinforcement learning (RL) is to let an agent learn an optimal control policy in an unknown environment so that future expected rewards are maximized. The model-free RL approach directly learns the policy based on data samples. Although using many samples tends to improve the accuracy of policy learning, c…
A deep learning method for regression without model assumptions.
A new model-free subsampling method using uniform designs is proposed.
SAVE combines Q-learning and MCTS with amortized value estimates for improved performance.
Combines model-based and model-free RL for better financial market performance.
We provide the first solution for model-free reinforcement learning of ω-regular objectives for Markov decision processes (MDPs). We present a constructive reduction from the almost-sure satisfaction of ω-regular objectives to an almost- sure reachability problem and extend this technique to learning how to control an …
Improved model-free reinforcement learning with decision-estimation coefficient.
The field of reinforcement learning (RL) is facing increasingly challenging domains with combinatorial complexity. For an RL agent to address these challenges, it is essential that it can plan effectively. Prior work has typically utilized an explicit model of the environment, combined with a specific planning algorith…
We prove the existence and uniqueness of solutions of SDEs with Lipschitz coefficients, driven by continuous, model-free martingales. The main tool in our reasoning is Picard's iterative procedure and a model-free version of the Burkholder-Davis-Gundy inequality for integrals driven by model-free, continuous martingale…
Two new algorithms improve model-free RL for infinite-horizon MDPs.
A new method selects a representative subsample for efficient kernel density estimation.
This paper improves robot grasping by integrating meta-control and latent-space imagination.
Model-based reinforcement learning (RL) algorithms can attain excellent sample efficiency, but often lag behind the best model-free algorithms in terms of asymptotic performance. This is especially true with high-capacity parametric function approximators, such as deep networks. In this paper, we study how to bridge th…
The paper tackles model-based RL's inaccuracy issue by dynamically adjusting planning horizons.
A new model-free algorithm achieves near-optimal regret for infinite-horizon MDPs.
Identifying the location of a disturbance and its magnitude is an important component for stable operation of power systems. We study the problem of localizing and estimating a disturbance in the interconnected power system. We take a model-free approach to this problem by using frequency data from generators. Specific…
A model-free framework extracts risk-neutral densities from short-dated options.
Bayesian approach improves -greedy exploration in RL.
The study compares reinforcement learning models and finds model-based approaches superior for complex MDPs.
FORK improves model-free reinforcement learning performance.
We present two different approaches to stochastic integration in frictionless model free financial mathematics. The first one is in the spirit of Itô's integral and based on a certain topology which is induced by the outer measure corresponding to the minimal superhedging price. The second one is based on the controlle…
Adversarial methods for imitation learning have been shown to perform well on various control tasks. However, they require a large number of environment interactions for convergence. In this paper, we propose an end-to-end differentiable adversarial imitation learning algorithm in a Dyna-like framework for switching be…
Forecasting a time series from multivariate predictors constitutes a challenging problem, especially using model-free approaches. Most techniques, such as nearest-neighbor prediction, quickly suffer from the curse of dimensionality and overfitting for more than a few predictors which has limited their application mostl…