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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 26–50 of 137 papers

TitleStatusHype
Beyond CVaR: Leveraging Static Spectral Risk Measures for Enhanced Decision-Making in Distributional Reinforcement LearningCode0
Hedging and Pricing Structured Products Featuring Multiple Underlying Assets—0
Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning—0
Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space—0
Offline and Distributional Reinforcement Learning for Radio Resource Management—0
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
Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model—0
More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning—0
Echoes of Socratic Doubt: Embracing Uncertainty in Calibrated Evidential Reinforcement LearningCode0
Distributional Off-policy Evaluation with Bellman Residual MinimizationCode0
A Robust Quantile Huber Loss With Interpretable Parameter Adjustment In Distributional Reinforcement LearningCode0
Distributional Reinforcement Learning-based Energy Arbitrage Strategies in Imbalance Settlement Mechanism—0
Noise Distribution Decomposition based Multi-Agent Distributional Reinforcement Learning—0
Distributional Bellman Operators over Mean EmbeddingsCode0
An introduction to reinforcement learning for neuroscience—0
Beyond Average Return in Markov Decision Processes—0
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