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 10351–10375 of 15113 papers

TitleStatusHype
Offline Pre-trained Multi-Agent Decision Transformer—0
Offline Primal-Dual Reinforcement Learning for Linear MDPs—0
Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes—0
Offline Reinforcement Learning and Sequence Modeling for Downlink Link Adaptation—0
Offline Reinforcement Learning as Anti-Exploration—0
Offline Reinforcement Learning at Multiple Frequencies—0
Offline Reinforcement Learning for Human-Guided Human-Machine Interaction with Private Information—0
Offline reinforcement learning for job-shop scheduling problems—0
Offline Reinforcement Learning for Large Scale Language Action Spaces—0
Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management—0
Offline Reinforcement Learning for Mobile Notifications—0
Offline Reinforcement Learning for Road Traffic Control—0
Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets—0
Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation—0
Offline Reinforcement Learning Hands-On—0
Offline Reinforcement Learning Under Value and Density-Ratio Realizability: The Power of Gaps—0
Offline Reinforcement Learning with Pseudometric Learning—0
Offline reinforcement learning with uncertainty for treatment strategies in sepsis—0
Offline Reinforcement Learning with Realizability and Single-policy Concentrability—0
Offline Reinforcement Learning with Differential Privacy—0
Offline Reinforcement Learning with Instrumental Variables in Confounded Markov Decision Processes—0
Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient—0
Offline Reinforcement Learning with Imbalanced Datasets—0
Offline Reinforcement Learning with Behavioral Supervisor Tuning—0
Offline Reinforcement Learning with Adaptive Behavior Regularization—0
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Benchmark Results

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