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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 71–80 of 137 papers

TitleStatusHype
The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning—0
Risk Perspective Exploration in Distributional Reinforcement Learning—0
Robust Reinforcement Learning with Distributional Risk-averse formulation—0
IGN : Implicit Generative NetworksCode0
A Simulation Environment and Reinforcement Learning Method for Waste Reduction—0
Interpretable Stochastic Model Predictive Control using Distributional Reinforced Estimation for Quadrotor Tracking Systems—0
Gamma and Vega Hedging Using Deep Distributional Reinforcement LearningCode1
Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement LearningCode1
Distributional Reinforcement Learning for Scheduling of Chemical Production Processes—0
Exploration with Multi-Sample Target Values for Distributional Reinforcement Learning—0
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