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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 51–75 of 137 papers

TitleStatusHype
Invariance to Quantile Selection in Distributional Continuous Control—0
Is Risk-Sensitive Reinforcement Learning Properly Resolved?—0
Learning Risk-Aware Quadrupedal Locomotion using Distributional Reinforcement Learning—0
Millimeter Wave Communications with an Intelligent Reflector: Performance Optimization and Distributional Reinforcement Learning—0
Minimizing Safety Interference for Safe and Comfortable Automated Driving with Distributional Reinforcement Learning—0
MMD-MIX: Value Function Factorisation with Maximum Mean Discrepancy for Cooperative Multi-Agent Reinforcement Learning—0
More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning—0
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
Nonlinear Distributional Gradient Temporal-Difference Learning—0
Normality-Guided Distributional Reinforcement Learning for Continuous Control—0
Offline and Distributional Reinforcement Learning for Radio Resource Management—0
Offline and Distributional Reinforcement Learning for Wireless Communications—0
One-Step Distributional Reinforcement Learning—0
On Policy Evaluation Algorithms in Distributional Reinforcement Learning—0
On solutions of the distributional Bellman equation—0
PACER: A Fully Push-forward-based Distributional Reinforcement Learning Algorithm—0
PG-Rainbow: Using Distributional Reinforcement Learning in Policy Gradient Methods—0
Pitfall of Optimism: Distributional Reinforcement Learning by Randomizing Risk Criterion—0
Policy Evaluation in Distributional LQR—0
Policy Gradient Methods for Risk-Sensitive Distributional Reinforcement Learning with Provable Convergence—0
Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation—0
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