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 7351–7400 of 15113 papers

TitleStatusHype
L2C2: Locally Lipschitz Continuous Constraint towards Stable and Smooth Reinforcement Learning—0
Deep Reinforcement Learning Based Multi-Access Edge Computing Schedule for Internet of Vehicle—0
Learning to Mitigate AI Collusion on Economic Platforms—0
Energy-Efficient Parking Analytics System using Deep Reinforcement LearningCode0
Learning Reward Models for Cooperative Trajectory Planning with Inverse Reinforcement Learning and Monte Carlo Tree SearchCode0
Convex Programs and Lyapunov Functions for Reinforcement Learning: A Unified Perspective on the Analysis of Value-Based Methods—0
Statistical Inference After Adaptive Sampling for Longitudinal Data—0
Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization—0
Robust Policy Learning over Multiple Uncertainty Sets—0
Reinforcement Learning in Presence of Discrete Markovian Context Evolution—0
Sequential Bayesian experimental designs via reinforcement learning—0
Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation—0
Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality—0
Motivating Physical Activity via Competitive Human-Robot Interaction—0
Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost—0
Deep Reinforcement Learning and Convex Mean-Variance Optimisation for Portfolio Management—0
Individual-Level Inverse Reinforcement Learning for Mean Field Games—0
Goal Recognition as Reinforcement LearningCode0
Autonomous Drone Swarm Navigation and Multi-target Tracking in 3D Environments with Dynamic Obstacles—0
End-to-end Reinforcement Learning of Robotic Manipulation with Robust Keypoints Representation—0
Neural NID Rules—0
Robust Learning from Observation with Model MisspecificationCode0
Rate-matching the regret lower-bound in the linear quadratic regulator with unknown dynamics—0
Understanding Curriculum Learning in Policy Optimization for Online Combinatorial OptimizationCode0
Regularized Q-learning—0
Computational-Statistical Gaps in Reinforcement Learning—0
A Unified Perspective on Value Backup and Exploration in Monte-Carlo Tree Search—0
Group-Agent Reinforcement Learning—0
AI-based Robust Resource Allocation in End-to-End Network Slicing under Demand and CSI Uncertainties—0
Interpretable pipelines with evolutionarily optimized modules for RL tasks with visual inputs—0
Abstraction for Deep Reinforcement Learning—0
Universal Learning Waveform Selection Strategies for Adaptive Target Tracking—0
Settling the Communication Complexity for Distributed Offline Reinforcement Learning—0
Understanding Value Decomposition Algorithms in Deep Cooperative Multi-Agent Reinforcement Learning—0
SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition—0
Uncovering Instabilities in Variational-Quantum Deep Q-NetworksCode0
Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory—0
Reinforcement Learning in the Wild: Scalable RL Dispatching Algorithm Deployed in Ridehailing Marketplace—0
Scenario-Assisted Deep Reinforcement Learning—0
Understanding and Shifting Preferences for Battery Electric Vehicles—0
Offline Reinforcement Learning with Realizability and Single-policy Concentrability—0
Transferred Q-learning—0
Bayesian Nonparametrics for Offline Skill DiscoveryCode0
Intelligent Autonomous Intersection Management—0
A Reinforcement Learning Approach to Domain-Knowledge Inclusion Using Grammar Guided Symbolic RegressionCode0
Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence—0
Energy Management Based on Multi-Agent Deep Reinforcement Learning for A Multi-Energy Industrial Park—0
Local Explanations for Reinforcement Learning—0
PolicyCleanse: Backdoor Detection and Mitigation in Reinforcement Learning—0
GrASP: Gradient-Based Affordance Selection for Planning—0
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Benchmark Results

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