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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 1–25 of 137 papers

TitleStatusHype
Conservative Offline Distributional Reinforcement LearningCode1
A Distributional Analogue to the Successor RepresentationCode1
Intelligent Resource Allocation in Joint Radar-Communication With Graph Neural NetworksCode1
Risk-Sensitive Policy with Distributional Reinforcement LearningCode1
Distributional Reinforcement Learning with Unconstrained Monotonic Neural NetworksCode1
Distributional Reinforcement Learning via Moment MatchingCode1
Trust Region-Based Safe Distributional Reinforcement Learning for Multiple ConstraintsCode1
Implicit Distributional Reinforcement LearningCode1
Unifying Cardiovascular Modelling with Deep Reinforcement Learning for Uncertainty Aware Control of Sepsis TreatmentCode1
Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement LearningCode1
Gamma and Vega Hedging Using Deep Distributional Reinforcement LearningCode1
An introduction to reinforcement learning for neuroscience—0
Bellman Unbiasedness: Toward Provably Efficient Distributional Reinforcement Learning with General Value Function Approximation—0
A Point-Based Algorithm for Distributional Reinforcement Learning in Partially Observable Domains—0
An Analysis of Quantile Temporal-Difference Learning—0
An Analysis of Categorical Distributional Reinforcement Learning—0
Cramer Type Distances for Learning Gaussian Mixture Models by Gradient Descent—0
CTRLS: Chain-of-Thought Reasoning via Latent State-Transition—0
A Local Temporal Difference Code for Distributional Reinforcement Learning—0
Adaptive Nesterov Accelerated Distributional Deep Hedging for Efficient Volatility Risk Management—0
Beyond Average Return in Markov Decision Processes—0
Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Bounds—0
Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space—0
Conservative Distributional Reinforcement Learning with Safety Constraints—0
A Distributional Perspective on Actor-Critic Framework—0
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