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 5726–5750 of 15113 papers

TitleStatusHype
SAM-R1: Leveraging SAM for Reward Feedback in Multimodal Segmentation via Reinforcement Learning—0
SAM-RL: Sensing-Aware Model-Based Reinforcement Learning via Differentiable Physics-Based Simulation and Rendering—0
SAPO-RL: Sequential Actuator Placement Optimization for Fuselage Assembly via Reinforcement Learning—0
SARI: Structured Audio Reasoning via Curriculum-Guided Reinforcement Learning—0
SAT-MARL: Specification Aware Training in Multi-Agent Reinforcement Learning—0
SatNet: A Benchmark for Satellite Scheduling Optimization—0
Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation—0
Say What I Want: Towards the Dark Side of Neural Dialogue Models—0
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation—0
^2-exploration for Reinforcement Learning—0
Scaffolding Reflection in Reinforcement Learning Framework for Confinement Escape Problem—0
Scalable and Incremental Learning of Gaussian Mixture Models—0
Scalable and Sample Efficient Distributed Policy Gradient Algorithms in Multi-Agent Networked Systems—0
Scalable Bayesian Inverse Reinforcement Learning by Auto-Encoding Reward—0
Scalable Centralized Deep Multi-Agent Reinforcement Learning via Policy Gradients—0
Scalable Communication for Multi-Agent Reinforcement Learning via Transformer-Based Email Mechanism—0
Scalable, Decentralized Multi-Agent Reinforcement Learning Methods Inspired by Stigmergy and Ant Colonies—0
Scalable Deep Reinforcement Learning Algorithms for Mean Field Games—0
Scalable Deep Reinforcement Learning for Routing and Spectrum Access in Physical Layer—0
Scalable Deep Reinforcement Learning for Ride-Hailing—0
Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot—0
Scalable Evolution Strategies Pipeline for Solving the Vehicle Routing Problem—0
Scalable Fragment-Based 3D Molecular Design with Reinforcement Learning—0
Scalable Grid-Aware Dynamic Matching using Deep Reinforcement Learning—0
Scalable Joint Learning of Wireless Multiple-Access Policies and their Signaling—0
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Benchmark Results

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