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 8276–8300 of 15113 papers

TitleStatusHype
Offline Learning of Counterfactual Predictions for Real-World Robotic Reinforcement Learning—0
Offline Meta-level Model-based Reinforcement Learning Approach for Cold-Start Recommendation—0
Offline Model-Based Reinforcement Learning with Anti-Exploration—0
Offline Multi-Agent Reinforcement Learning with Coupled Value Factorization—0
Offline Multitask Representation Learning for Reinforcement Learning—0
Offline Multi-task Transfer RL with Representational Penalization—0
Offline-Online Reinforcement Learning: Extending Batch and Online RL—0
Offline-Online Reinforcement Learning for Energy Pricing in Office Demand Response: Lowering Energy and Data Costs—0
Offline Policy Evaluation and Optimization under Confounding—0
Offline Policy Optimization in RL with Variance Regularizaton—0
Offline Policy Optimization with Variance Regularization—0
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
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Benchmark Results

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