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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
Multi-compartment Neuron and Population Encoding Powered Spiking Neural Network for Deep Distributional Reinforcement Learning—0
Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model—0
Noise Distribution Decomposition based Multi-Agent Distributional Reinforcement Learning—0
Non-Crossing Quantile Regression for Distributional Reinforcement Learning—0
Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning—0
Distributional Model Equivalence for Risk-Sensitive Reinforcement LearningCode0
Distributional Off-policy Evaluation with Bellman Residual MinimizationCode0
A Cramér Distance perspective on Quantile Regression based Distributional Reinforcement LearningCode0
Fully Parameterized Quantile Function for Distributional Reinforcement LearningCode0
Conjugated Discrete Distributions for Distributional Reinforcement LearningCode0
Distributional Reinforcement Learning for Energy-Based Sequential ModelsCode0
Tackling Uncertainties in Multi-Agent Reinforcement Learning through Integration of Agent Termination DynamicsCode0
GAN Q-learningCode0
Distributional Reinforcement Learning for Multi-Dimensional Reward FunctionsCode0
The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement LearningCode0
QUOTA: The Quantile Option Architecture for Reinforcement LearningCode0
Distributional Reinforcement Learning with Regularized Wasserstein LossCode0
Value-Distributional Model-Based Reinforcement LearningCode0
Distributional Reinforcement Learning with Quantile RegressionCode0
IGN : Implicit Generative NetworksCode0
Distributional constrained reinforcement learning for supply chain optimizationCode0
Distributional Bellman Operators over Mean EmbeddingsCode0
Beyond CVaR: Leveraging Static Spectral Risk Measures for Enhanced Decision-Making in Distributional Reinforcement LearningCode0
Implicit Quantile Networks for Distributional Reinforcement LearningCode0
Constrained Reinforcement Learning using Distributional Representation for Trustworthy Quadrotor UAV Tracking ControlCode0
ADDQ: Adaptive Distributional Double Q-LearningCode0
Echoes of Socratic Doubt: Embracing Uncertainty in Calibrated Evidential Reinforcement LearningCode0
CTD4 -- A Deep Continuous Distributional Actor-Critic Agent with a Kalman Fusion of Multiple CriticsCode0
Estimating Risk and Uncertainty in Deep Reinforcement LearningCode0
Estimation and Inference in Distributional Reinforcement LearningCode0
EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement LearningCode0
Information-Directed Exploration for Deep Reinforcement LearningCode0
A Robust Quantile Huber Loss With Interpretable Parameter Adjustment In Distributional Reinforcement LearningCode0
Exploring the Training Robustness of Distributional Reinforcement Learning against Noisy State ObservationsCode0
RIZE: Regularized Imitation Learning via Distributional Reinforcement LearningCode0
Two steps to risk sensitivityCode0
Variance Control for Distributional Reinforcement LearningCode0
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