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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 91–100 of 137 papers

TitleStatusHype
Toward Risk-based Optimistic Exploration for Cooperative Multi-Agent Reinforcement Learning—0
The Benefits of Being Categorical Distributional: Uncertainty-aware Regularized Exploration in Reinforcement Learning—0
Towards Understanding Distributional Reinforcement Learning: Regularization, Optimization, Acceleration and Sinkhorn Algorithm—0
Uncertainty-Aware Transient Stability-Constrained Preventive Redispatch: A Distributional Reinforcement Learning Approach—0
SENTINEL: Taming Uncertainty with Ensemble-based Distributional Reinforcement Learning—0
A Comparative Analysis of Expected and Distributional Reinforcement Learning—0
Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning—0
Adaptive Nesterov Accelerated Distributional Deep Hedging for Efficient Volatility Risk Management—0
Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for Automated Driving using Distributional Reinforcement Learning—0
A Distributional Perspective on Actor-Critic Framework—0
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