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Distributional Reinforcement Learning

Value distribution is the distribution of the random return received by a reinforcement learning agent. it been used for a specific purpose such as implementing risk-aware behaviour.

We have random return Z whose expectation is the value Q. This random return is also described by a recursive equation, but one of a distributional nature

Papers

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Showing 1–25 of 137 papers

TitleStatusHype
Second-Order Bounds for [0,1]-Valued Regression via Betting Loss—0
Distributional Reinforcement Learning on Path-dependent Options—0
CTRLS: Chain-of-Thought Reasoning via Latent State-Transition—0
ADDQ: Adaptive Distributional Double Q-LearningCode0
A Point-Based Algorithm for Distributional Reinforcement Learning in Partially Observable Domains—0
Flow Models for Unbounded and Geometry-Aware Distributional Reinforcement Learning—0
Deep Distributional Learning with Non-crossing Quantile Network—0
Offline and Distributional Reinforcement Learning for Wireless Communications—0
RIZE: Regularized Imitation Learning via Distributional Reinforcement LearningCode0
Adaptive Nesterov Accelerated Distributional Deep Hedging for Efficient Volatility Risk Management—0
A Finite Sample Analysis of Distributional TD Learning with Linear Function Approximation—0
Robust Probabilistic Model Checking with Continuous Reward Domains—0
Tackling Uncertainties in Multi-Agent Reinforcement Learning through Integration of Agent Termination DynamicsCode0
Risk-averse policies for natural gas futures trading using distributional reinforcement learning—0
Beyond CVaR: Leveraging Static Spectral Risk Measures for Enhanced Decision-Making in Distributional Reinforcement LearningCode0
Hedging and Pricing Structured Products Featuring Multiple Underlying Assets—0
Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning—0
Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space—0
Offline and Distributional Reinforcement Learning for Radio Resource Management—0
Foundations of Multivariate Distributional Reinforcement Learning—0
EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement LearningCode0
Bellman Unbiasedness: Toward Provably Efficient Distributional Reinforcement Learning with General Value Function Approximation—0
On Policy Evaluation Algorithms in Distributional Reinforcement Learning—0
PG-Rainbow: Using Distributional Reinforcement Learning in Policy Gradient Methods—0
Policy Gradient Methods for Risk-Sensitive Distributional Reinforcement Learning with Provable Convergence—0
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