Papers Distributional Reinforcement Learning
“Distributional Reinforcement Learning” 태그가 달린 논문 137편 · 필터 해제
Distributional Reinforcement Learning on Path-dependent Options
We reinterpret and propose a framework for pricing path-dependent financial derivatives by estimating the full distribution of payoffs using Distributional Reinforcement Learning (DistRL). Unlike traditional methods that…
Distributional Reinforcement Learningreinforcement-learningReinforcement LearningUncertainty QuantificationSecond-Order Bounds for [0,1]-Valued Regression via Betting Loss
We consider the $[0,1]$-valued regression problem in the i.i.d. setting. In a related problem called cost-sensitive classification, \citet{foster21efficient} have shown that the log loss minimizer achieves an improved ge…
Distributional Reinforcement LearningregressionCTRLS: Chain-of-Thought Reasoning via Latent State-Transition
Chain-of-thought (CoT) reasoning enables large language models (LLMs) to break down complex problems into interpretable intermediate steps, significantly enhancing model transparency and performance in reasoning tasks. H…
Distributional Reinforcement Learningreinforcement-learningReinforcement LearningADDQ: Adaptive Distributional Double Q-Learning
Bias problems in the estimation of $Q$-values are a well-known obstacle that slows down convergence of $Q$-learning and actor-critic methods. One of the reasons of the success of modern RL algorithms is partially a direc…
Distributional Reinforcement LearningMuJoCoQ-LearningA Point-Based Algorithm for Distributional Reinforcement Learning in Partially Observable Domains
In many real-world planning tasks, agents must tackle uncertainty about the environment's state and variability in the outcomes of any chosen policy. We address both forms of uncertainty as a first step toward safer algo…
Decision MakingDistributional Reinforcement LearningFlow Models for Unbounded and Geometry-Aware Distributional Reinforcement Learning
We introduce a new architecture for Distributional Reinforcement Learning (DistRL) that models return distributions using normalizing flows. This approach enables flexible, unbounded support for return distributions, in …
Distributional Reinforcement LearningDeep Distributional Learning with Non-crossing Quantile Network
In this paper, we introduce a non-crossing quantile (NQ) network for conditional distribution learning. By leveraging non-negative activation functions, the NQ network ensures that the learned distributions remain monoto…
Distributional Reinforcement Learningquantile regressionReinforcement Learning (RL)Offline and Distributional Reinforcement Learning for Wireless Communications
The rapid growth of heterogeneous and massive wireless connectivity in 6G networks demands intelligent solutions to ensure scalability, reliability, privacy, ultra-low latency, and effective control. Although artificial …
Distributional Reinforcement LearningManagementquantile regressionreinforcement-learning+2RIZE: Regularized Imitation Learning via Distributional Reinforcement Learning
We introduce a novel Inverse Reinforcement Learning (IRL) approach that overcomes limitations of fixed reward assignments and constrained flexibility in implicit reward regularization. By extending the Maximum Entropy IR…
Distributional Reinforcement LearningImitation LearningMuJoCoreinforcement-learning+1Adaptive Nesterov Accelerated Distributional Deep Hedging for Efficient Volatility Risk Management
In the field of financial derivatives trading, managing volatility risk is crucial for protecting investment portfolios from market changes. Traditional Vega hedging strategies, which often rely on basic and rule-based m…
Distributional Reinforcement LearningManagementreinforcement-learningReinforcement LearningA Finite Sample Analysis of Distributional TD Learning with Linear Function Approximation
In this paper, we study the finite-sample statistical rates of distributional temporal difference (TD) learning with linear function approximation. The aim of distributional TD learning is to estimate the return distribu…
Distributional Reinforcement LearningRobust Probabilistic Model Checking with Continuous Reward Domains
Probabilistic model checking traditionally verifies properties on the expected value of a measure of interest. This restriction may fail to capture the quality of service of a significant proportion of a system's runs, e…
Distributional Reinforcement LearningmodelTackling Uncertainties in Multi-Agent Reinforcement Learning through Integration of Agent Termination Dynamics
Multi-Agent Reinforcement Learning (MARL) has gained significant traction for solving complex real-world tasks, but the inherent stochasticity and uncertainty in these environments pose substantial challenges to efficien…
Distributional Reinforcement LearningMulti-agent Reinforcement Learningreinforcement-learningReinforcement Learning+2Risk-averse policies for natural gas futures trading using distributional reinforcement learning
Financial markets have experienced significant instabilities in recent years, creating unique challenges for trading and increasing interest in risk-averse strategies. Distributional Reinforcement Learning (RL) algorithm…
Distributional Reinforcement Learningenergy tradingquantile regressionReinforcement Learning (RL)Beyond CVaR: Leveraging Static Spectral Risk Measures for Enhanced Decision-Making in Distributional Reinforcement Learning
In domains such as finance, healthcare, and robotics, managing worst-case scenarios is critical, as failure to do so can lead to catastrophic outcomes. Distributional Reinforcement Learning (DRL) provides a natural frame…
Decision MakingDistributional Reinforcement LearningHedging and Pricing Structured Products Featuring Multiple Underlying Assets
Hedging a portfolio containing autocallable notes presents unique challenges due to the complex risk profile of these financial instruments. In addition to hedging, pricing these notes, particularly when multiple underly…
Distributional Reinforcement LearningReinforcement Learning (RL)Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning
When decisions are made at high frequency, traditional reinforcement learning (RL) methods struggle to accurately estimate action values. In turn, their performance is inconsistent and often poor. Whether the performance…
Distributional Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space
Deep Generative Models (DGMs), including Energy-Based Models (EBMs) and Score-based Generative Models (SGMs), have advanced high-fidelity data generation and complex continuous distribution approximation. However, their …
Decision MakingDistributional Reinforcement LearningReinforcement Learning (RL)Offline and Distributional Reinforcement Learning for Radio Resource Management
Reinforcement learning (RL) has proved to have a promising role in future intelligent wireless networks. Online RL has been adopted for radio resource management (RRM), taking over traditional schemes. However, due to it…
Distributional Reinforcement LearningManagementreinforcement-learningReinforcement Learning+1Foundations of Multivariate Distributional Reinforcement Learning
In reinforcement learning (RL), the consideration of multivariate reward signals has led to fundamental advancements in multi-objective decision-making, transfer learning, and representation learning. This work introduce…
Decision MakingDistributional Reinforcement Learningreinforcement-learningReinforcement Learning+3