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 2901–2950 of 15113 papers

TitleStatusHype
Attention-based Reinforcement Learning for Real-Time UAV Semantic Communication—0
Attention-based QoE-aware Digital Twin Empowered Edge Computing for Immersive Virtual Reality—0
AI-driven materials design: a mini-review—0
Deep Reinforcement Learning for Adaptive Caching in Hierarchical Content Delivery Networks—0
Attention-based Fault-tolerant Approach for Multi-agent Reinforcement Learning Systems—0
AAMDRL: Augmented Asset Management with Deep Reinforcement Learning—0
Attention-based Deep Reinforcement Learning for Multi-view Environments—0
AI-based traffic analysis in digital twin networks—0
Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic States—0
Decision-making Strategy on Highway for Autonomous Vehicles using Deep Reinforcement Learning—0
Decision Mamba: Reinforcement Learning via Hybrid Selective Sequence Modeling—0
Decoding Polar Codes with Reinforcement Learning—0
Deep Distributional Learning with Non-crossing Quantile Network—0
Attention-Aware Face Hallucination via Deep Reinforcement Learning—0
Attention-Aware Deep Reinforcement Learning for Video Face Recognition—0
AI-based Robust Resource Allocation in End-to-End Network Slicing under Demand and CSI Uncertainties—0
Attentional Policies for Cross-Context Multi-Agent Reinforcement Learning—0
AI-based Resource Allocation: Reinforcement Learning for Adaptive Auto-scaling in Serverless Environments—0
AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control—0
Attend2Pack: Bin Packing through Deep Reinforcement Learning with Attention—0
AI-based Radio Resource Management and Trajectory Design for PD-NOMA Communication in IRS-UAV Assisted Networks—0
Attacking Deep Reinforcement Learning-Based Traffic Signal Control Systems with Colluding Vehicles—0
AI Assisted Annotator using Reinforcement Learning—0
Adaptive Batch Size for Safe Policy Gradients—0
Attacking and Defending Deep Reinforcement Learning Policies—0
AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving—0
AttackGNN: Red-Teaming GNNs in Hardware Security Using Reinforcement Learning—0
A* Tree Search for Portfolio Management—0
ACECODER: Acing Coder RL via Automated Test-Case Synthesis—0
A Hysteretic Q-learning Coordination Framework for Emerging Mobility Systems in Smart Cities—0
A Transferable Approach for Partitioning Machine Learning Models on Multi-Chip-Modules—0
Constrained Reinforcement Learning Has Zero Duality Gap—0
A Transferable and Automatic Tuning of Deep Reinforcement Learning for Cost Effective Phishing Detection—0
Adaptive and Multiple Time-scale Eligibility Traces for Online Deep Reinforcement Learning—0
Deciding What's Fair: Challenges of Applying Reinforcement Learning in Online Marketplaces—0
Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning—0
Decision ConvFormer: Local Filtering in MetaFormer is Sufficient for Decision Making—0
ATraDiff: Accelerating Online Reinforcement Learning with Imaginary Trajectories—0
A Tractable Algorithm For Finite-Horizon Continuous Reinforcement Learning—0
A Hybrid PAC Reinforcement Learning Algorithm—0
A Hybrid Neuro-Symbolic approach for Text-Based Games using Inductive Logic Programming—0
Adaptive Aggregation for Safety-Critical Control—0
Deceptive Reinforcement Learning for Privacy-Preserving Planning—0
INTAGS: Interactive Agent-Guided Simulation—0
At Human Speed: Deep Reinforcement Learning with Action Delay—0
Adaptive Adversarial Training for Meta Reinforcement Learning—0
A Hybrid Approach for Reinforcement Learning Using Virtual Policy Gradient for Balancing an Inverted Pendulum—0
A Fast Convergence Theory for Offline Decision Making—0
ACE: An Actor Ensemble Algorithm for Continuous Control with Tree Search—0
AACC: Asymmetric Actor-Critic in Contextual Reinforcement Learning—0
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Benchmark Results

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