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 3826–3850 of 15113 papers

TitleStatusHype
Learning Graph-Enhanced Commander-Executor for Multi-Agent NavigationCode0
Predictable MDP Abstraction for Unsupervised Model-Based RLCode1
Non-zero-sum Game Control for Multi-vehicle Driving via Reinforcement LearningCode0
Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback—0
Online Reinforcement Learning with Uncertain Episode Lengths—0
Optimizing Audio Recommendations for the Long-Term: A Reinforcement Learning Perspective—0
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority InfluenceCode1
Adaptive Aggregation for Safety-Critical Control—0
Ensemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning—0
Multi-Task Recommendations with Reinforcement LearningCode1
Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR—0
Transfer learning for process design with reinforcement learning—0
Towards Skilled Population Curriculum for Multi-Agent Reinforcement Learning—0
Intrinsic Rewards from Self-Organizing Feature Maps for Exploration in Reinforcement LearningCode0
Holistic Deep-Reinforcement-Learning-based Training of Autonomous Navigation Systems—0
RLTP: Reinforcement Learning to Pace for Delayed Impression Modeling in Preloaded Ads—0
State-wise Safe Reinforcement Learning: A Survey—0
A Strong Baseline for Batch Imitation Learning—0
Grounding Large Language Models in Interactive Environments with Online Reinforcement LearningCode2
DITTO: Offline Imitation Learning with World Models—0
Offline Learning in Markov Games with General Function Approximation—0
Efficient Online Reinforcement Learning with Offline DataCode2
Arena-Web -- A Web-based Development and Benchmarking Platform for Autonomous Navigation Approaches—0
Offline Learning of Closed-Loop Deep Brain Stimulation Controllers for Parkinson Disease TreatmentCode0
Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage—0
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Benchmark Results

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