SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 111–120 of 137 papers

TitleStatusHype
Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Bounds—0
Conservative Distributional Reinforcement Learning with Safety Constraints—0
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
CTRLS: Chain-of-Thought Reasoning via Latent State-Transition—0
Deep Distributional Learning with Non-crossing Quantile Network—0
Deep Reinforcement Learning for Artificial Upwelling Energy Management—0
Demand-Side Scheduling Based on Multi-Agent Deep Actor-Critic Learning for Smart Grids—0
Distributional Perturbation for Efficient Exploration in Distributional Reinforcement Learning—0
Distributional Reinforcement Learning-based Energy Arbitrage Strategies in Imbalance Settlement Mechanism—0
Show:102550
← PrevPage 12 of 14Next →

No leaderboard results yet.