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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 111–120 of 137 papers

Title	Date	Tasks	Status
Distributional Reinforcement Learning for Energy-Based Sequential Models	Dec 18, 2019	Distributional Reinforcement Learningreinforcement-learning	CodeCode Available
Tackling Uncertainties in Multi-Agent Reinforcement Learning through Integration of Agent Termination Dynamics	Jan 21, 2025	Distributional Reinforcement LearningMulti-agent Reinforcement Learning	CodeCode Available
GAN Q-learning	May 13, 2018	Distributional Reinforcement LearningOpenAI Gym	CodeCode Available
Distributional Reinforcement Learning for Multi-Dimensional Reward Functions	Oct 26, 2021	Distributional Reinforcement Learningreinforcement-learning	CodeCode Available
The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning	May 25, 2023	Distributional Reinforcement LearningOffline RL	CodeCode Available
QUOTA: The Quantile Option Architecture for Reinforcement Learning	Nov 5, 2018	Decision MakingDistributional Reinforcement Learning	CodeCode Available
Distributional Reinforcement Learning with Regularized Wasserstein Loss	Feb 1, 2022	Atari GamesDistributional Reinforcement Learning	CodeCode Available
Value-Distributional Model-Based Reinforcement Learning	Aug 12, 2023	continuous-controlContinuous Control	CodeCode Available
Distributional Reinforcement Learning with Quantile Regression	Oct 27, 2017	Atari GamesDistributional Reinforcement Learning	CodeCode Available
IGN : Implicit Generative Networks	Jun 13, 2022	Atari GamesDistributional Reinforcement Learning	CodeCode Available

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