SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 9701–9750 of 15113 papers

TitleStatusHype
Grid-Interactive Multi-Zone Building Control Using Reinforcement Learning with Global-Local Policy Search—0
Random Network Distillation as a Diversity Metric for Both Image and Text Generation—0
Measuring Visual Generalization in Continuous Control from PixelsCode1
Model-Based Reinforcement Learning for Type 1Diabetes Blood Glucose Control—0
Balancing Constraints and Rewards with Meta-Gradient D4PG—0
Average Cost Optimal Control of Stochastic Systems Using Reinforcement Learning—0
Deep Reinforcement Learning for Real-Time Optimization of Pumps in Water Distribution SystemsCode1
Deep Reinforcement Learning and Transportation Research: A Comprehensive Review—0
Efficient Wasserstein Natural Gradients for Reinforcement LearningCode1
Human-centric Dialog Training via Offline Reinforcement Learning—0
Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning?—0
Smaller World Models for Reinforcement Learning—0
AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control—0
Local Search for Policy Iteration in Continuous Control—0
The Greatest Teacher, Failure is: Using Reinforcement Learning for SFC Placement Based on Availability and Energy Consumption—0
Remote Electrical Tilt Optimization via Safe Reinforcement Learning—0
Nearly Minimax Optimal Reward-free Reinforcement Learning—0
Deep-Reinforcement-Learning-Based Scheduling with Contiguous Resource Allocation for Next-Generation Cellular Systems—0
Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks—0
Distributed Resource Allocation with Multi-Agent Deep Reinforcement Learning for 5G-V2V CommunicationCode1
Contrastive Explanations for Reinforcement Learning via Embedded Self PredictionsCode0
Safe Reinforcement Learning with Natural Language Constraints—0
Reinforcement Learning on Computational Resource Allocation of Cloud-based Wireless Networks—0
Robust Constrained-MDPs: Soft-Constrained Robust Policy Optimization under Model UncertaintyCode0
Trust the Model When It Is Confident: Masked Model-based Actor-Critic—0
MS-Ranker: Accumulating Evidence from Potentially Correct Candidates for Answer Selection—0
Deep Reinforcement Learning for Asset Allocation in US Equities—0
Graph Convolutional Value Decomposition in Multi-Agent Reinforcement LearningCode1
Characterizing Policy Divergence for Personalized Meta-Reinforcement Learning—0
Parameterized Reinforcement Learning for Optical System Optimization—0
Instance Weighted Incremental Evolution Strategies for Reinforcement Learning in Dynamic EnvironmentsCode0
Deep RL With Information Constrained Policies: Generalization in Continuous Control—0
Learning to Locomote: Understanding How Environment Design Matters for Deep Reinforcement Learning—0
LaND: Learning to Navigate from DisengagementsCode1
Jointly-Learned State-Action Embedding for Efficient Reinforcement Learning—0
Dynamic Context Selection for Document-level Neural Machine Translation via Reinforcement Learning—0
EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological ModelsCode1
Learning Intrinsic Symbolic Rewards in Reinforcement Learning—0
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer LearningCode1
Maximum Reward Formulation In Reinforcement LearningCode0
Trajectory Inspection: A Method for Iterative Clinician-Driven Design of Reinforcement Learning StudiesCode1
Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and BaselinesCode1
Nonstationary Reinforcement Learning with Linear Function Approximation—0
Provable Fictitious Play for General Mean-Field Games—0
Information-Driven Adaptive Sensing Based on Deep Reinforcement LearningCode0
Actor-Critic Algorithm for High-dimensional Partial Differential Equations—0
Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving—0
Regularized Inverse Reinforcement Learning—0
Online Safety Assurance for Deep Reinforcement Learning—0
Variational Intrinsic Control Revisited—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified