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 6901–6950 of 15113 papers

TitleStatusHype
Learning Multiresolution Matrix Factorization and its Wavelet Networks on GraphsCode0
Robust Dynamic Bus Control: A Distributional Multi-agent Reinforcement Learning Approach—0
OnSlicing: Online End-to-End Network Slicing with Reinforcement Learning—0
Integrating Pretrained Language Model for Dialogue Policy Learning—0
Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics—0
Learning Large Neighborhood Search Policy for Integer ProgrammingCode1
Rewards with Negative Examples for Reinforced Topic-Focused Abstractive Summarization—0
A Collaborative Multi-agent Reinforcement Learning Framework for Dialog Action Decomposition—0
Feedback Attribution for Counterfactual Bandit Learning in Multi-Domain Spoken Language Understanding—0
A Generative Framework for Simultaneous Machine Translation—0
Neuro-Symbolic Approaches for Text-Based Policy LearningCode0
Learning Task Sampling Policy for Multitask Learning—0
Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure—0
Learning to Operate an Electric Vehicle Charging Station Considering Vehicle-grid Integration—0
Human-Level Control without Server-Grade HardwareCode0
Machine Learning aided Crop Yield Optimization—0
Settling the Horizon-Dependence of Sample Complexity in Reinforcement Learning—0
Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments—0
An Actor-Critic Method for Simulation-Based Optimization—0
Decentralized Multi-Agent Reinforcement Learning: An Off-Policy Method—0
Learning Coordinated Terrain-Adaptive Locomotion by Imitating a Centroidal Dynamics Planner—0
Adjacency constraint for efficient hierarchical reinforcement learning—0
A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles—0
Convergence and Optimality of Policy Gradient Methods in Weakly Smooth Settings—0
Intrusion Prevention through Optimal StoppingCode1
Context Meta-Reinforcement Learning via NeuromodulationCode0
On Joint Learning for Solving Placement and Routing in Chip DesignCode1
Reinforced Workload Distribution Fairness—0
Mixed Cooperative-Competitive Communication Using Multi-Agent Reinforcement Learning—0
Learning to Communicate with Reinforcement Learning for an Adaptive Traffic Control System—0
GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL—0
Attacking Video Recognition Models with Bullet-Screen CommentsCode1
Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning—0
Adaptive Discretization in Online Reinforcement Learning—0
Data Informed Residual Reinforcement Learning for High-Dimensional Robotic Tracking Control—0
Open Problem: Tight Online Confidence Intervals for RKHS Elements—0
Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision ProcessesCode0
Efficient Meta Subspace OptimizationCode0
URLB: Unsupervised Reinforcement Learning BenchmarkCode1
D2RLIR : an improved and diversified ranking function in interactive recommendation systems based on deep reinforcement learning—0
An Adaptable Approach to Learn Realistic Legged Locomotion without Examples—0
Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives—0
Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning—0
Extracting Expert's Goals by What-if Interpretable Modeling—0
Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning—0
A Law of Iterated Logarithm for Multi-Agent Reinforcement Learning—0
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection—0
Stabilising viscous extensional flows using Reinforcement LearningCode0
The ODE Method for Asymptotic Statistics in Stochastic Approximation and Reinforcement Learning—0
Model based Multi-agent Reinforcement Learning with Tensor Decompositions—0
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Benchmark Results

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