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 1551–1575 of 15113 papers

TitleStatusHype
Asynchronous Fractional Multi-Agent Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing—0
A random measure approach to reinforcement learning in continuous time—0
Revisiting Space Mission Planning: A Reinforcement Learning-Guided Approach for Multi-Debris Rendezvous—0
OffRIPP: Offline RL-based Informative Path Planning—0
On-orbit Servicing for Spacecraft Collision Avoidance With Autonomous Decision Making—0
Offline and Distributional Reinforcement Learning for Radio Resource Management—0
Learning with Dynamics: Autonomous Regulation of UAV Based Communication Networks with Dynamic UAV Crew—0
Reinforcement Leaning for Infinite-Dimensional Systems—0
From Goal-Conditioned to Language-Conditioned Agents via Vision-Language Models—0
Stage-Wise Reward Shaping for Acrobatic Robots: A Constrained Multi-Objective Reinforcement Learning ApproachCode2
Development and Validation of Heparin Dosing Policies Using an Offline Reinforcement Learning Algorithm—0
Whole-body End-Effector Pose Tracking—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
Intelligent Routing Algorithm over SDN: Reusable Reinforcement Learning Approach—0
CANDERE-COACH: Reinforcement Learning from Noisy Feedback—0
A novel agent with formal goal-reaching guarantees: an experimental study with a mobile robot—0
A Distribution-Aware Flow-Matching for Generating Unstructured Data for Few-Shot Reinforcement Learning—0
OMG-RL:Offline Model-based Guided Reward Learning for Heparin Treatment—0
Scalable Multi-agent Reinforcement Learning for Factory-wide Dynamic Scheduling—0
SoloParkour: Constrained Reinforcement Learning for Visual Locomotion from Privileged Experience—0
MAGICS: Adversarial RL with Minimax Actors Guided by Implicit Critic Stackelberg for Convergent Neural Synthesis of Robot Safety—0
Disentangling Recognition and Decision Regrets in Image-Based Reinforcement Learning—0
Reinforcement Learning-based Model Predictive Control for Greenhouse Climate ControlCode1
Assessing the Zero-Shot Capabilities of LLMs for Action Evaluation in RL—0
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Benchmark Results

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