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 1026–1050 of 15113 papers

TitleStatusHype
Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement LearningCode2
EvoRL: A GPU-accelerated Framework for Evolutionary Reinforcement LearningCode7
Faster Machine Translation Ensembling with Reinforcement Learning and Competitive Correction—0
Data Center Cooling System Optimization Using Offline Reinforcement Learning—0
Towards Efficient Multi-Objective Optimisation for Real-World Power Grid Topology Control—0
Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks—0
Coordinating Ride-Pooling with Public Transit using Reward-Guided Conservative Q-Learning: An Offline Training and Online Fine-Tuning Reinforcement Learning Framework—0
An Attentive Graph Agent for Topology-Adaptive Cyber DefenceCode1
Large Language Model driven Policy Exploration for Recommender Systems—0
To Measure or Not: A Cost-Sensitive, Selective Measuring Environment for Agricultural Management Decisions with Reinforcement LearningCode0
Kimi k1.5: Scaling Reinforcement Learning with LLMsCode7
State Combinatorial Generalization In Decision Making With Conditional Diffusion Models—0
MONA: Myopic Optimization with Non-myopic Approval Can Mitigate Multi-step Reward Hacking—0
Deep Reinforcement Learning with Hybrid Intrinsic Reward Model—0
Adaptive Data Exploitation in Deep Reinforcement LearningCode0
Exploring the Technology Landscape through Topic Modeling, Expert Involvement, and Reinforcement Learning—0
On Generalization and Distributional Update for Mimicking Observations with Adequate Exploration—0
Reinforcement learning Based Automated Design of Differential Evolution Algorithm for Black-box Optimization—0
Evolution and The Knightian Blindspot of Machine Learning—0
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement LearningCode15
AdaWM: Adaptive World Model based Planning for Autonomous Driving—0
Reinforcement Learning Constrained Beam Search for Parameter Optimization of Paper Drying Under Flexible Constraints—0
RL-RC-DoT: A Block-level RL agent for Task-Aware Video Compression—0
Extend Adversarial Policy Against Neural Machine Translation via Unknown Token—0
Improving thermal state preparation of Sachdev-Ye-Kitaev model with reinforcement learning on quantum hardwareCode0
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Benchmark Results

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