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 4276–4300 of 15113 papers

TitleStatusHype
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning—0
DRED: Zero-Shot Transfer in Reinforcement Learning via Data-Regularised Environment DesignCode0
Contrastive Diffuser: Planning Towards High Return States via Contrastive Learning—0
Frugal Actor-Critic: Sample Efficient Off-Policy Deep Reinforcement Learning Using Unique Experiences—0
Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short DelaysCode0
Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgentCode0
Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation ProblemCode0
Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate—0
Abstracted Trajectory Visualization for Explainability in Reinforcement Learning—0
Assessing the Impact of Distribution Shift on Reinforcement Learning Performance—0
Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach—0
DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching—0
A Safe Reinforcement Learning driven Weights-varying Model Predictive Control for Autonomous Vehicle Motion Control—0
The Virtues of Pessimism in Inverse Reinforcement Learning—0
Adaptive Q-Aid for Conditional Supervised Learning in Offline Reinforcement Learning—0
A Survey of Constraint Formulations in Safe Reinforcement Learning—0
An Auction-based Marketplace for Model Trading in Federated Learning—0
Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems—0
Rethinking the Role of Proxy Rewards in Language Model AlignmentCode0
To the Max: Reinventing Reward in Reinforcement LearningCode0
The RL/LLM Taxonomy Tree: Reviewing Synergies Between Reinforcement Learning and Large Language Models—0
The Political Preferences of LLMs—0
Leveraging Approximate Model-based Shielding for Probabilistic Safety Guarantees in Continuous EnvironmentsCode0
Expert Proximity as Surrogate Rewards for Single Demonstration Imitation LearningCode0
Causal Coordinated Concurrent Reinforcement Learning—0
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Benchmark Results

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