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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
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
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
Distributional Perturbation for Efficient Exploration in Distributional Reinforcement Learning—0
Towards Understanding Distributional Reinforcement Learning: Regularization, Optimization, Acceleration and Sinkhorn Algorithm—0
Exploring the Robustness of Distributional Reinforcement Learning against Noisy State Observations—0
Distributional Reinforcement Learning with Monotonic Splines—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
Conservative Offline Distributional Reinforcement LearningCode1
MMD-MIX: Value Function Factorisation with Maximum Mean Discrepancy for Cooperative Multi-Agent Reinforcement Learning—0
Automatic Risk Adaptation in Distributional Reinforcement Learning—0
Distributional Reinforcement Learning with Unconstrained Monotonic Neural NetworksCode1
Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning—0
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