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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 31–40 of 137 papers

TitleStatusHype
Foundations of Multivariate Distributional Reinforcement Learning—0
EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement LearningCode0
Bellman Unbiasedness: Toward Provably Efficient Distributional Reinforcement Learning with General Value Function Approximation—0
On Policy Evaluation Algorithms in Distributional Reinforcement Learning—0
PG-Rainbow: Using Distributional Reinforcement Learning in Policy Gradient Methods—0
Policy Gradient Methods for Risk-Sensitive Distributional Reinforcement Learning with Provable Convergence—0
CTD4 -- A Deep Continuous Distributional Actor-Critic Agent with a Kalman Fusion of Multiple CriticsCode0
Statistical Efficiency of Distributional Temporal Difference Learning and Freedman's Inequality in Hilbert Spaces—0
Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation—0
Uncertainty-Aware Transient Stability-Constrained Preventive Redispatch: A Distributional Reinforcement Learning Approach—0
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