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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 101–137 of 137 papers

TitleStatusHype
MMD-MIX: Value Function Factorisation with Maximum Mean Discrepancy for Cooperative Multi-Agent Reinforcement Learning—0
Automatic Risk Adaptation in Distributional Reinforcement Learning—0
Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning—0
Bayesian Distributional Policy Gradients—0
Safe Distributional Reinforcement Learning—0
SENTINEL: Taming Uncertainty with Ensemble-based Distributional Reinforcement Learning—0
Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for Automated Driving using Distributional Reinforcement Learning—0
Controlling Synthetic Characters in Simulations: A Case for Cognitive Architectures and Sigma—0
Distributional Reinforcement Learning for Risk-Sensitive Policies—0
A Distributional Perspective on Actor-Critic Framework—0
Batch-Constrained Distributional Reinforcement Learning for Session-based Recommendation—0
A Local Temporal Difference Code for Distributional Reinforcement Learning—0
Non-Crossing Quantile Regression for Distributional Reinforcement Learning—0
Distributional Reinforcement Learning for mmWave Communications with Intelligent Reflectors on a UAV—0
Demand-Side Scheduling Based on Multi-Agent Deep Actor-Critic Learning for Smart Grids—0
Improving Robustness via Risk Averse Distributional Reinforcement Learning—0
Distributional Reinforcement Learning with Ensembles—0
Millimeter Wave Communications with an Intelligent Reflector: Performance Optimization and Distributional Reinforcement Learning—0
Sample-based Distributional Policy Gradient—0
Distributional Reinforcement Learning for Energy-Based Sequential ModelsCode0
Fully Parameterized Quantile Function for Distributional Reinforcement LearningCode0
Estimating Risk and Uncertainty in Deep Reinforcement LearningCode0
Stochastically Dominant Distributional Reinforcement Learning—0
Distributional Reinforcement Learning for Efficient Exploration—0
GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing—0
Statistics and Samples in Distributional Reinforcement Learning—0
Distributional reinforcement learning with linear function approximation—0
A Comparative Analysis of Expected and Distributional Reinforcement Learning—0
Information-Directed Exploration for Deep Reinforcement LearningCode0
QUOTA: The Quantile Option Architecture for Reinforcement LearningCode0
Implicit Quantile Networks for Distributional Reinforcement LearningCode0
Nonlinear Distributional Gradient Temporal-Difference Learning—0
GAN Q-learningCode0
Exploration by Distributional Reinforcement Learning—0
An Analysis of Categorical Distributional Reinforcement Learning—0
Distributional Reinforcement Learning with Quantile RegressionCode0
The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning—0
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