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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
Controlling Synthetic Characters in Simulations: A Case for Cognitive Architectures and Sigma—0
Cramer Type Distances for Learning Gaussian Mixture Models by Gradient Descent—0
An introduction to reinforcement learning for neuroscience—0
CTRLS: Chain-of-Thought Reasoning via Latent State-Transition—0
Deep Distributional Learning with Non-crossing Quantile Network—0
Distributional Reinforcement Learning with Online Risk-awareness Adaption—0
Exploration by Distributional Reinforcement Learning—0
Bayesian Distributional Policy Gradients—0
Batch-Constrained Distributional Reinforcement Learning for Session-based Recommendation—0
Distributional Reinforcement Learning with Monotonic Splines—0
Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning—0
Distributional Perturbation for Efficient Exploration in Distributional Reinforcement Learning—0
Distributional Reinforcement Learning-based Energy Arbitrage Strategies in Imbalance Settlement Mechanism—0
Distributional Reinforcement Learning for Efficient Exploration—0
Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space—0
Distributional Reinforcement Learning for Risk-Sensitive Policies—0
Distributional Reinforcement Learning for mmWave Communications with Intelligent Reflectors on a UAV—0
Automatic Risk Adaptation in Distributional Reinforcement Learning—0
Distributional Reinforcement Learning for Scheduling of Chemical Production Processes—0
Distributional Reinforcement Learning on Path-dependent Options—0
Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Bounds—0
A Comparative Analysis of Expected and Distributional Reinforcement Learning—0
Distributional reinforcement learning with linear function approximation—0
Distributional Reinforcement Learning with Ensembles—0
Demand-Side Scheduling Based on Multi-Agent Deep Actor-Critic Learning for Smart Grids—0
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