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 6226–6250 of 15113 papers

TitleStatusHype
End-to-end Reinforcement Learning of Robotic Manipulation with Robust Keypoints Representation—0
Neural NID Rules—0
A Unified Perspective on Value Backup and Exploration in Monte-Carlo Tree Search—0
Choices, Risks, and Reward Reports: Charting Public Policy for Reinforcement Learning SystemsCode1
Computational-Statistical Gaps in Reinforcement Learning—0
Rate-matching the regret lower-bound in the linear quadratic regulator with unknown dynamics—0
The Shapley Value in Machine LearningCode1
Online Decision TransformerCode2
Understanding Curriculum Learning in Policy Optimization for Online Combinatorial OptimizationCode0
Regularized Q-learning—0
Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory—0
Universal Learning Waveform Selection Strategies for Adaptive Target Tracking—0
SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition—0
Reinforcement Learning in the Wild: Scalable RL Dispatching Algorithm Deployed in Ridehailing Marketplace—0
Abstraction for Deep Reinforcement Learning—0
AI-based Robust Resource Allocation in End-to-End Network Slicing under Demand and CSI Uncertainties—0
Group-Agent Reinforcement Learning—0
Interpretable pipelines with evolutionarily optimized modules for RL tasks with visual inputs—0
Uncovering Instabilities in Variational-Quantum Deep Q-NetworksCode0
Understanding Value Decomposition Algorithms in Deep Cooperative Multi-Agent Reinforcement Learning—0
Settling the Communication Complexity for Distributed Offline Reinforcement Learning—0
Understanding and Shifting Preferences for Battery Electric Vehicles—0
Transferred Q-learning—0
Offline Reinforcement Learning with Realizability and Single-policy Concentrability—0
Rethinking Goal-conditioned Supervised Learning and Its Connection to Offline RLCode1
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Benchmark Results

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