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 3761–3770 of 15113 papers

TitleStatusHype
AutoDOViz: Human-Centered Automation for Decision Optimization—0
Generalization in Visual Reinforcement Learning with the Reward Sequence DistributionCode0
Reinforcement Learning in the Wild with Maximum Likelihood-based Model Transfer—0
HOPE: Human-Centric Off-Policy Evaluation for E-Learning and Healthcare—0
Natural Language-conditioned Reinforcement Learning with Inside-out Task Language Development and Translation—0
Effective Multimodal Reinforcement Learning with Modality Alignment and Importance Enhancement—0
Efficient Exploration via Epistemic-Risk-Seeking Policy Optimization—0
Promoting Cooperation in Multi-Agent Reinforcement Learning via Mutual Help—0
Mixed Traffic Control and Coordination from Pixels—0
Deep Reinforcement Learning for mmWave Initial Beam Alignment—0
Show:102550
← PrevPage 377 of 1512Next →

Benchmark Results

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