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 3451–3475 of 15113 papers

TitleStatusHype
Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning—0
Offline Inverse Constrained Reinforcement Learning for Safe-Critical Decision Making in Healthcare—0
Avoiding mode collapse in diffusion models fine-tuned with reinforcement learning—0
Exploring Natural Language-Based Strategies for Efficient Number Learning in Children through Reinforcement LearningCode0
Efficient Reinforcement Learning with Large Language Model Priors—0
Masked Generative Priors Improve World Models Sequence Modelling Capabilities—0
VerifierQ: Enhancing LLM Test Time Compute with Q-Learning-based Verifiers—0
Offline Hierarchical Reinforcement Learning via Inverse Optimization—0
Probabilistic Satisfaction of Temporal Logic Constraints in Reinforcement Learning via Adaptive Policy-Switching—0
Flipping-based Policy for Chance-Constrained Markov Decision Processes—0
Crafting desirable climate trajectories with RL explored socio-environmental simulationsCode0
Zero-Shot Generalization of Vision-Based RL Without Data Augmentation—0
A Safety Modulator Actor-Critic Method in Model-Free Safe Reinforcement Learning and Application in UAV Hovering—0
MotionRL: Align Text-to-Motion Generation to Human Preferences with Multi-Reward Reinforcement Learning—0
Q-WSL: Optimizing Goal-Conditioned RL with Weighted Supervised Learning via Dynamic Programming—0
Solving Multi-Goal Robotic Tasks with Decision Transformer—0
On the Modeling Capabilities of Large Language Models for Sequential Decision Making—0
Reinforcement Learning From Imperfect Corrective Actions And Proxy Rewards—0
Solving robust MDPs as a sequence of static RL problems—0
Towards using Reinforcement Learning for Scaling and Data Replication in Cloud Systems—0
AlphaRouter: Quantum Circuit Routing with Reinforcement Learning and Tree Search—0
Towards Measuring Goal-Directedness in AI Systems—0
Improved Off-policy Reinforcement Learning in Biological Sequence DesignCode0
DeepLTL: Learning to Efficiently Satisfy Complex LTL Specifications for Multi-Task RL—0
Data-driven Under Frequency Load Shedding Using Reinforcement Learning—0
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Benchmark Results

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