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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 51–75 of 137 papers

TitleStatusHype
Diverse Projection Ensembles for Distributional Reinforcement Learning—0
PACER: A Fully Push-forward-based Distributional Reinforcement Learning Algorithm—0
Improving the generalizability and robustness of large-scale traffic signal control—0
The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation—0
Distributional Reinforcement Learning with Dual Expectile-Quantile Regression—0
The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement LearningCode0
One-Step Distributional Reinforcement Learning—0
Policy Evaluation in Distributional LQR—0
Toward Risk-based Optimistic Exploration for Cooperative Multi-Agent Reinforcement Learning—0
Constrained Reinforcement Learning using Distributional Representation for Trustworthy Quadrotor UAV Tracking ControlCode0
Distributional constrained reinforcement learning for supply chain optimizationCode0
Trust Region-Based Safe Distributional Reinforcement Learning for Multiple ConstraintsCode1
Multi-compartment Neuron and Population Encoding Powered Spiking Neural Network for Deep Distributional Reinforcement Learning—0
An Analysis of Quantile Temporal-Difference Learning—0
Risk-Sensitive Policy with Distributional Reinforcement LearningCode1
Invariance to Quantile Selection in Distributional Continuous Control—0
Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Bounds—0
Intelligent Resource Allocation in Joint Radar-Communication With Graph Neural NetworksCode1
How Does Return Distribution in Distributional Reinforcement Learning Help Optimization?—0
Normality-Guided Distributional Reinforcement Learning for Continuous Control—0
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
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