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 7376–7400 of 15113 papers

TitleStatusHype
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