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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 76–100 of 137 papers

TitleStatusHype
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
Interpretable Stochastic Model Predictive Control using Distributional Reinforced Estimation for Quadrotor Tracking Systems—0
Distributional Reinforcement Learning for Scheduling of Chemical Production Processes—0
Exploration with Multi-Sample Target Values for Distributional Reinforcement Learning—0
Distributional Reinforcement Learning with Regularized Wasserstein LossCode0
On solutions of the distributional Bellman equation—0
Conservative Distributional Reinforcement Learning with Safety Constraints—0
Robustness and risk management via distributional dynamic programming—0
Conjugated Discrete Distributions for Distributional Reinforcement LearningCode0
Two steps to risk sensitivityCode0
Distributional Reinforcement Learning for Multi-Dimensional Reward FunctionsCode0
The Benefits of Being Categorical Distributional: Uncertainty-aware Regularized Exploration in Reinforcement Learning—0
A Cramér Distance perspective on Quantile Regression based Distributional Reinforcement LearningCode0
Exploring the Robustness of Distributional Reinforcement Learning against Noisy State Observations—0
Towards Understanding Distributional Reinforcement Learning: Regularization, Optimization, Acceleration and Sinkhorn Algorithm—0
Distributional Reinforcement Learning with Monotonic Splines—0
Distributional Perturbation for Efficient Exploration in Distributional Reinforcement Learning—0
Exploring the Training Robustness of Distributional Reinforcement Learning against Noisy State ObservationsCode0
Minimizing Safety Interference for Safe and Comfortable Automated Driving with Distributional Reinforcement Learning—0
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