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 3701–3750 of 15113 papers

TitleStatusHype
Cost-Effective Two-Stage Network Slicing for Edge-Cloud Orchestrated Vehicular Networks—0
Deep Reinforcement Learning for QoS-Constrained Resource Allocation in Multiservice Networks—0
A State Representation Dueling Network for Deep Reinforcement Learning—0
Deep reinforcement learning for RAN optimization and control—0
Agent-Agnostic Human-in-the-Loop Reinforcement Learning—0
A State Augmentation based approach to Reinforcement Learning from Human Preferences—0
Deep Reinforcement Learning for Real-Time Ground Delay Program Revision and Corresponding Flight Delay Assignments—0
Deep Reinforcement Learning for Resource Management in Network Slicing—0
Cost-Aware Dynamic Cloud Workflow Scheduling using Self-Attention and Evolutionary Reinforcement Learning—0
Deep Reinforcement Learning for RIS-Assisted FD Systems: Single or Distributed RIS?—0
DisCo RL: Distribution-Conditioned Reinforcement Learning for General-Purpose Policies—0
Deep Reinforcement Learning for Robotic Manipulation-The state of the art—0
Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment—0
Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes—0
Deep Reinforcement Learning for Routing a Heterogeneous Fleet of Vehicles—0
Deep Reinforcement Learning for Safe Landing Site Selection with Concurrent Consideration of Divert Maneuvers—0
Deep Reinforcement Learning for Scalable Multiagent Spacecraft Inspection—0
Using Deep Reinforcement Learning for mmWave Real-Time Scheduling—0
Deep reinforcement learning for scheduling in large-scale networked control systems—0
Deep Reinforcement Learning for Scheduling in Cellular Networks—0
Deep reinforcement learning for search, recommendation, and online advertising: a survey—0
Autonomous Unmanned Aerial Vehicle Navigation using Reinforcement Learning: A Systematic Review—0
Costate-focused models for reinforcement learning—0
Deep Reinforcement Learning for Shared Autonomous Vehicles (SAV) Fleet Management—0
Deep Reinforcement Learning for Simultaneous Sensing and Channel Access in Cognitive Networks—0
Deep Reinforcement Learning for Single-Shot Diagnosis and Adaptation in Damaged Robots—0
Deep Decentralized Reinforcement Learning for Cooperative Control—0
Autonomous Warehouse Robot using Deep Q-Learning—0
Discounted Reinforcement Learning Is Not an Optimization Problem—0
Deep Reinforcement Learning for Smart Home Energy Management—0
A State Aggregation Approach for Solving Knapsack Problem with Deep Reinforcement Learning—0
Accelerating Stochastic Composition Optimization—0
Corruption-Robust Offline Reinforcement Learning—0
Deep Reinforcement Learning for Stochastic Computation Offloading in Digital Twin Networks—0
Corruption-robust exploration in episodic reinforcement learning—0
Adaptive patch foraging in deep reinforcement learning agents—0
Autoregressive Multi-trait Essay Scoring via Reinforcement Learning with Scoring-aware Multiple Rewards—0
Deep Reinforcement Learning for System-on-Chip: Myths and Realities—0
A stabilizing reinforcement learning approach for sampled systems with partially unknown models—0
Deep Reinforcement Learning for Task Offloading in UAV-Aided Smart Farm Networks—0
Selective Network Discovery via Deep Reinforcement Learning on Embedded Spaces—0
Deep Reinforcement Learning for Tensegrity Robot Locomotion—0
Automated Video Game Testing Using Synthetic and Human-Like Agents—0
Deep Reinforcement Learning for Time Scheduling in RF-Powered Backscatter Cognitive Radio Networks—0
Discourse-Aware Neural Rewards for Coherent Text Generation—0
Discovering an Aid Policy to Minimize Student Evasion Using Offline Reinforcement Learning—0
Auto-tuning Distributed Stream Processing Systems using Reinforcement Learning—0
Deep Reinforcement Learning for Trading—0
Autotuning PID control using Actor-Critic Deep Reinforcement Learning—0
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes—0
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Benchmark Results

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