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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

Showing 126137 of 137 papers

TitleStatusHype
ADDQ: Adaptive Distributional Double Q-LearningCode0
Echoes of Socratic Doubt: Embracing Uncertainty in Calibrated Evidential Reinforcement LearningCode0
CTD4 -- A Deep Continuous Distributional Actor-Critic Agent with a Kalman Fusion of Multiple CriticsCode0
Estimating Risk and Uncertainty in Deep Reinforcement LearningCode0
Estimation and Inference in Distributional Reinforcement LearningCode0
EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement LearningCode0
Information-Directed Exploration for Deep Reinforcement LearningCode0
A Robust Quantile Huber Loss With Interpretable Parameter Adjustment In Distributional Reinforcement LearningCode0
Exploring the Training Robustness of Distributional Reinforcement Learning against Noisy State ObservationsCode0
RIZE: Regularized Imitation Learning via Distributional Reinforcement LearningCode0
Two steps to risk sensitivityCode0
Variance Control for Distributional Reinforcement LearningCode0
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