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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 21–30 of 137 papers

TitleStatusHype
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
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