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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
Risk-averse policies for natural gas futures trading using distributional reinforcement learning—0
Risk Perspective Exploration in Distributional Reinforcement Learning—0
Robustness and risk management via distributional dynamic programming—0
Robust Probabilistic Model Checking with Continuous Reward Domains—0
Robust Reinforcement Learning with Distributional Risk-averse formulation—0
Safe Distributional Reinforcement Learning—0
Sample-based Distributional Policy Gradient—0
Second-Order Bounds for [0,1]-Valued Regression via Betting Loss—0
Bellman Unbiasedness: Toward Provably Efficient Distributional Reinforcement Learning with General Value Function Approximation—0
Statistical Efficiency of Distributional Temporal Difference Learning and Freedman's Inequality in Hilbert Spaces—0
Statistics and Samples in Distributional Reinforcement Learning—0
Stochastically Dominant Distributional Reinforcement Learning—0
The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning—0
The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning—0
The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation—0
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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