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 1476–1500 of 15113 papers

TitleStatusHype
VerifierQ: Enhancing LLM Test Time Compute with Q-Learning-based Verifiers—0
Probabilistic Satisfaction of Temporal Logic Constraints in Reinforcement Learning via Adaptive Policy-Switching—0
Offline Hierarchical Reinforcement Learning via Inverse Optimization—0
Masked Generative Priors Improve World Models Sequence Modelling Capabilities—0
Offline Inverse Constrained Reinforcement Learning for Safe-Critical Decision Making in Healthcare—0
Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning—0
Efficient Reinforcement Learning with Large Language Model Priors—0
Crafting desirable climate trajectories with RL explored socio-environmental simulationsCode0
Zero-Shot Generalization of Vision-Based RL Without Data Augmentation—0
MotionRL: Align Text-to-Motion Generation to Human Preferences with Multi-Reward Reinforcement Learning—0
Flipping-based Policy for Chance-Constrained Markov Decision Processes—0
Q-WSL: Optimizing Goal-Conditioned RL with Weighted Supervised Learning via Dynamic Programming—0
A Safety Modulator Actor-Critic Method in Model-Free Safe Reinforcement Learning and Application in UAV Hovering—0
Retrieval-Augmented Decision Transformer: External Memory for In-context RLCode1
On the Modeling Capabilities of Large Language Models for Sequential Decision Making—0
Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement LearningCode1
Solving robust MDPs as a sequence of static RL problems—0
Solving Multi-Goal Robotic Tasks with Decision Transformer—0
Reinforcement Learning From Imperfect Corrective Actions And Proxy Rewards—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
GreenLight-Gym: Reinforcement learning benchmark environment for control of greenhouse production systemsCode1
Data-driven Under Frequency Load Shedding Using Reinforcement Learning—0
Improved Off-policy Reinforcement Learning in Biological Sequence DesignCode0
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Benchmark Results

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