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 51–100 of 137 papers

TitleStatusHype
Invariance to Quantile Selection in Distributional Continuous Control—0
Is Risk-Sensitive Reinforcement Learning Properly Resolved?—0
Learning Risk-Aware Quadrupedal Locomotion using Distributional Reinforcement Learning—0
Millimeter Wave Communications with an Intelligent Reflector: Performance Optimization and Distributional Reinforcement Learning—0
Minimizing Safety Interference for Safe and Comfortable Automated Driving with Distributional Reinforcement Learning—0
MMD-MIX: Value Function Factorisation with Maximum Mean Discrepancy for Cooperative Multi-Agent Reinforcement Learning—0
More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning—0
Multi-compartment Neuron and Population Encoding Powered Spiking Neural Network for Deep Distributional Reinforcement Learning—0
Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model—0
Noise Distribution Decomposition based Multi-Agent Distributional Reinforcement Learning—0
Non-Crossing Quantile Regression for Distributional Reinforcement Learning—0
Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning—0
Nonlinear Distributional Gradient Temporal-Difference Learning—0
Normality-Guided Distributional Reinforcement Learning for Continuous Control—0
Offline and Distributional Reinforcement Learning for Radio Resource Management—0
Offline and Distributional Reinforcement Learning for Wireless Communications—0
One-Step Distributional Reinforcement Learning—0
On Policy Evaluation Algorithms in Distributional Reinforcement Learning—0
On solutions of the distributional Bellman equation—0
PACER: A Fully Push-forward-based Distributional Reinforcement Learning Algorithm—0
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
Safe Distributional Reinforcement Learning—0
Sample-based Distributional Policy Gradient—0
Second-Order Bounds for [0,1]-Valued Regression via Betting Loss—0
Bellman Unbiasedness: Toward Provably Efficient Distributional Reinforcement Learning with General Value Function Approximation—0
Statistical Efficiency of Distributional Temporal Difference Learning and Freedman's Inequality in Hilbert Spaces—0
Statistics and Samples in Distributional Reinforcement Learning—0
Stochastically Dominant Distributional Reinforcement Learning—0
The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning—0
The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning—0
The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation—0
Toward Risk-based Optimistic Exploration for Cooperative Multi-Agent Reinforcement Learning—0
The Benefits of Being Categorical Distributional: Uncertainty-aware Regularized Exploration in Reinforcement Learning—0
Towards Understanding Distributional Reinforcement Learning: Regularization, Optimization, Acceleration and Sinkhorn Algorithm—0
Uncertainty-Aware Transient Stability-Constrained Preventive Redispatch: A Distributional Reinforcement Learning Approach—0
SENTINEL: Taming Uncertainty with Ensemble-based Distributional Reinforcement Learning—0
A Comparative Analysis of Expected and Distributional Reinforcement Learning—0
Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning—0
Adaptive Nesterov Accelerated Distributional Deep Hedging for Efficient Volatility Risk Management—0
Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for Automated Driving using Distributional Reinforcement Learning—0
A Distributional Perspective on Actor-Critic Framework—0
Show:102550
← PrevPage 2 of 3Next →

No leaderboard results yet.