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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
Towards Understanding Distributional Reinforcement Learning: Regularization, Optimization, Acceleration and Sinkhorn Algorithm—0
Exploring the Robustness of Distributional Reinforcement Learning against Noisy State Observations—0
Distributional Reinforcement Learning with Monotonic Splines—0
Exploring the Training Robustness of Distributional Reinforcement Learning against Noisy State ObservationsCode0
Minimizing Safety Interference for Safe and Comfortable Automated Driving with Distributional Reinforcement Learning—0
Conservative Offline Distributional Reinforcement LearningCode1
MMD-MIX: Value Function Factorisation with Maximum Mean Discrepancy for Cooperative Multi-Agent Reinforcement Learning—0
Automatic Risk Adaptation in Distributional Reinforcement Learning—0
Distributional Reinforcement Learning with Unconstrained Monotonic Neural NetworksCode1
Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning—0
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