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 626–650 of 15113 papers

TitleStatusHype
xMTF: A Formula-Free Model for Reinforcement-Learning-Based Multi-Task Fusion in Recommender Systems—0
Stratified Expert Cloning with Adaptive Selection for User Retention in Large-Scale Recommender Systems—0
Smart Exploration in Reinforcement Learning using Bounded Uncertainty Models—0
The Role of Environment Access in Agnostic Reinforcement Learning—0
Algorithm Discovery With LLMs: Evolutionary Search Meets Reinforcement Learning—0
Concise Reasoning via Reinforcement LearningCode1
Joint Pedestrian and Vehicle Traffic Optimization in Urban Environments using Reinforcement LearningCode1
Physics-informed Modularized Neural Network for Advanced Building Control by Deep Reinforcement Learning—0
Impact of Price Inflation on Algorithmic Collusion Through Reinforcement Learning Agents—0
OrbitZoo: Multi-Agent Reinforcement Learning Environment for Orbital Dynamics—0
Decision SpikeFormer: Spike-Driven Transformer for Decision Making—0
Algorithmic Prompt Generation for Diverse Human-like Teaming and Communication with Large Language Models—0
Improving Mixed-Criticality Scheduling with Reinforcement Learning—0
Offline and Distributional Reinforcement Learning for Wireless Communications—0
DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world EnvironmentsCode4
Enhanced Penalty-based Bidirectional Reinforcement Learning Algorithms—0
Dexterous Manipulation through Imitation Learning: A Survey—0
Learning Dual-Arm Coordination for Grasping Large Flat Objects—0
Rethinking RL Scaling for Vision Language Models: A Transparent, From-Scratch Framework and Comprehensive Evaluation SchemeCode2
Adapting World Models with Latent-State Dynamics Residuals—0
Reasoning Under 1 Billion: Memory-Augmented Reinforcement Learning for Large Language ModelsCode0
Multi-SWE-bench: A Multilingual Benchmark for Issue ResolvingCode3
MAD: A Magnitude And Direction Policy Parametrization for Stability Constrained Reinforcement LearningCode0
Inference-Time Scaling for Generalist Reward Modeling—0
Integrating Human Knowledge Through Action Masking in Reinforcement Learning for Operations Research—0
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Benchmark Results

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