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 1031–1040 of 15113 papers

TitleStatusHype
Coordinating Ride-Pooling with Public Transit using Reward-Guided Conservative Q-Learning: An Offline Training and Online Fine-Tuning Reinforcement Learning Framework—0
Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks—0
An Attentive Graph Agent for Topology-Adaptive Cyber DefenceCode1
Large Language Model driven Policy Exploration for Recommender Systems—0
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement LearningCode15
Adaptive Data Exploitation in Deep Reinforcement LearningCode0
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
Exploring the Technology Landscape through Topic Modeling, Expert Involvement, and Reinforcement Learning—0
To Measure or Not: A Cost-Sensitive, Selective Measuring Environment for Agricultural Management Decisions with Reinforcement LearningCode0
Show:102550
← PrevPage 104 of 1512Next →

Benchmark Results

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