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 3501–3550 of 15113 papers

TitleStatusHype
Analysis on Riemann Hypothesis with Cross Entropy Optimization and Reasoning—0
Grounded Curriculum Learning—0
Constrained Reinforcement Learning for Safe Heat Pump ControlCode0
Focus On What Matters: Separated Models For Visual-Based RL Generalization—0
Generalizing Consistency Policy to Visual RL with Prioritized Proximal Experience Regularization—0
Learning to Bridge the Gap: Efficient Novelty Recovery with Planning and Reinforcement Learning—0
Strongly-polynomial time and validation analysis of policy gradient methods—0
TemporalPaD: a reinforcement-learning framework for temporal feature representation and dimension reduction—0
Enhancing Spectrum Efficiency in 6G Satellite Networks: A GAIL-Powered Policy Learning via Asynchronous Federated Inverse Reinforcement Learning—0
Cost-Aware Dynamic Cloud Workflow Scheduling using Self-Attention and Evolutionary Reinforcement Learning—0
Climate Adaptation with Reinforcement Learning: Experiments with Flooding and Transportation in CopenhagenCode0
Autoregressive Multi-trait Essay Scoring via Reinforcement Learning with Scoring-aware Multiple Rewards—0
Optimizing Downlink C-NOMA Transmission with Movable Antennas: A DDPG-based Approach—0
LoopSR: Looping Sim-and-Real for Lifelong Policy Adaptation of Legged Robots—0
Asynchronous Fractional Multi-Agent Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing—0
Offline and Distributional Reinforcement Learning for Radio Resource Management—0
Spiders Based on Anxiety: How Reinforcement Learning Can Deliver Desired User Experience in Virtual Reality Personalized Arachnophobia TreatmentCode0
OffRIPP: Offline RL-based Informative Path Planning—0
Revisiting Space Mission Planning: A Reinforcement Learning-Guided Approach for Multi-Debris Rendezvous—0
On-orbit Servicing for Spacecraft Collision Avoidance With Autonomous Decision Making—0
Learning with Dynamics: Autonomous Regulation of UAV Based Communication Networks with Dynamic UAV Crew—0
A random measure approach to reinforcement learning in continuous time—0
Reinforcement Leaning for Infinite-Dimensional Systems—0
Whole-body End-Effector Pose Tracking—0
Development and Validation of Heparin Dosing Policies Using an Offline Reinforcement Learning Algorithm—0
From Goal-Conditioned to Language-Conditioned Agents via Vision-Language Models—0
Energy Saving in 6G O-RAN Using DQN-based xApp—0
Physics Enhanced Residual Policy Learning (PERPL) for safety cruising in mixed traffic platooning under actuator and communication delay—0
A novel agent with formal goal-reaching guarantees: an experimental study with a mobile robot—0
CANDERE-COACH: Reinforcement Learning from Noisy Feedback—0
Intelligent Routing Algorithm over SDN: Reusable Reinforcement Learning Approach—0
A Distribution-Aware Flow-Matching for Generating Unstructured Data for Few-Shot Reinforcement Learning—0
SoloParkour: Constrained Reinforcement Learning for Visual Locomotion from Privileged Experience—0
Scalable Multi-agent Reinforcement Learning for Factory-wide Dynamic Scheduling—0
OMG-RL:Offline Model-based Guided Reward Learning for Heparin Treatment—0
MAGICS: Adversarial RL with Minimax Actors Guided by Implicit Critic Stackelberg for Convergent Neural Synthesis of Robot Safety—0
TACO-RL: Task Aware Prompt Compression Optimization with Reinforcement Learning—0
Assessing the Zero-Shot Capabilities of LLMs for Action Evaluation in RL—0
Disentangling Recognition and Decision Regrets in Image-Based Reinforcement Learning—0
The Central Role of the Loss Function in Reinforcement Learning—0
Reinforcement Learning as an Improvement Heuristic for Real-World Production Scheduling—0
IMRL: Integrating Visual, Physical, Temporal, and Geometric Representations for Enhanced Food Acquisition—0
Data-Efficient Quadratic Q-Learning Using LMIs—0
An Enhanced-State Reinforcement Learning Algorithm for Multi-Task Fusion in Large-Scale Recommender Systems—0
On-policy Actor-Critic Reinforcement Learning for Multi-UAV Exploration—0
A Reinforcement Learning Environment for Automatic Code Optimization in the MLIR Compiler—0
Robust Reinforcement Learning with Dynamic Distortion Risk MeasuresCode0
Instigating Cooperation among LLM Agents Using Adaptive Information Modulation—0
Mitigating Partial Observability in Adaptive Traffic Signal Control with Transformers—0
Offline Reinforcement Learning for Learning to Dispatch for Job Shop SchedulingCode0
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Benchmark Results

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