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 5326–5350 of 15113 papers

TitleStatusHype
AutoDOViz: Human-Centered Automation for Decision Optimization—0
Auto.gov: Learning-based Governance for Decentralized Finance (DeFi)Code0
HOPE: Human-Centric Off-Policy Evaluation for E-Learning and Healthcare—0
Effective Multimodal Reinforcement Learning with Modality Alignment and Importance Enhancement—0
Efficient Exploration via Epistemic-Risk-Seeking Policy Optimization—0
Reinforcement Learning in the Wild with Maximum Likelihood-based Model Transfer—0
Promoting Cooperation in Multi-Agent Reinforcement Learning via Mutual Help—0
Natural Language-conditioned Reinforcement Learning with Inside-out Task Language Development and Translation—0
Post Reinforcement Learning InferenceCode0
Robot path planning using deep reinforcement learning—0
Deep Reinforcement Learning for mmWave Initial Beam Alignment—0
A State Augmentation based approach to Reinforcement Learning from Human Preferences—0
Mixed Traffic Control and Coordination from Pixels—0
Exploiting Unlabeled Data for Feedback Efficient Human Preference based Reinforcement Learning—0
Learning to Forecast Aleatoric and Epistemic Uncertainties over Long Horizon Trajectories—0
Data Driven Reward Initialization for Preference based Reinforcement Learning—0
Tuning computer vision models with task rewards—0
Quantum Computing Provides Exponential Regret Improvement in Episodic Reinforcement Learning—0
Optimal Sample Complexity of Reinforcement Learning for Mixing Discounted Markov Decision Processes—0
Meta-Reinforcement Learning via Exploratory Task Clustering—0
Prioritized offline Goal-swapping Experience Replay—0
Reinforcement Learning Based Power Grid Day-Ahead Planning and AI-Assisted Control—0
Scalable Multi-Agent Reinforcement Learning with General Utilities—0
CERiL: Continuous Event-based Reinforcement Learning—0
Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications—0
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Benchmark Results

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