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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 71–80 of 137 papers

TitleStatusHype
PG-Rainbow: Using Distributional Reinforcement Learning in Policy Gradient Methods—0
Pitfall of Optimism: Distributional Reinforcement Learning by Randomizing Risk Criterion—0
Policy Evaluation in Distributional LQR—0
Policy Gradient Methods for Risk-Sensitive Distributional Reinforcement Learning with Provable Convergence—0
Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation—0
Risk-averse policies for natural gas futures trading using distributional reinforcement learning—0
Risk Perspective Exploration in Distributional Reinforcement Learning—0
Robustness and risk management via distributional dynamic programming—0
Robust Probabilistic Model Checking with Continuous Reward Domains—0
Robust Reinforcement Learning with Distributional Risk-averse formulation—0
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