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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
Improving Robustness via Risk Averse Distributional Reinforcement Learning—0
Improving the generalizability and robustness of large-scale traffic signal control—0
Interpretable Stochastic Model Predictive Control using Distributional Reinforced Estimation for Quadrotor Tracking Systems—0
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
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