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 976–1000 of 15113 papers

TitleStatusHype
Analytical Lyapunov Function Discovery: An RL-based Generative ApproachCode1
VolleyBots: A Testbed for Multi-Drone Volleyball Game Combining Motion Control and Strategic Play—0
RAPID: Robust and Agile Planner Using Inverse Reinforcement Learning for Vision-Based Drone Navigation—0
The Differences Between Direct Alignment Algorithms are a Blur—0
Preference VLM: Leveraging VLMs for Scalable Preference-Based Reinforcement Learning—0
Process Reinforcement through Implicit RewardsCode5
Reinforcement Learning with Segment Feedback—0
Reinforcement Learning for Long-Horizon Interactive LLM Agents—0
Dynamic object goal pushing with mobile manipulators through model-free constrained reinforcement learning—0
ACECODER: Acing Coder RL via Automated Test-Case Synthesis—0
Toward Task Generalization via Memory Augmentation in Meta-Reinforcement Learning—0
GNN-DT: Graph Neural Network Enhanced Decision Transformer for Efficient Optimization in Dynamic EnvironmentsCode1
Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning—0
Zeroth-order Informed Fine-Tuning for Diffusion Model: A Recursive Likelihood Ratio Optimizer—0
Recursive generalized type-2 fuzzy radial basis function neural networks for joint position estimation and adaptive EMG-based impedance control of lower limb exoskeletonsCode0
Learning from Suboptimal Data in Continuous Control via Auto-Regressive Soft Q-Network—0
Model-Free Predictive Control: Introductory Algebraic Calculations, and a Comparison with HEOL and ANNs—0
A Differentiated Reward Method for Reinforcement Learning based Multi-Vehicle Cooperative Decision-Making Algorithms—0
Optimizing Job Allocation using Reinforcement Learning with Graph Neural Networks—0
Decorrelated Soft Actor-Critic for Efficient Deep Reinforcement Learning—0
SHARPIE: A Modular Framework for Reinforcement Learning and Human-AI Interaction ExperimentsCode1
O-MAPL: Offline Multi-agent Preference Learning—0
Towards Physiologically Sensible Predictions via the Rule-based Reinforcement Learning Layer—0
RLS3: RL-Based Synthetic Sample Selection to Enhance Spatial Reasoning in Vision-Language Models for Indoor Autonomous Perception—0
Test-Time Training Scaling Laws for Chemical Exploration in Drug DesignCode3
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Benchmark Results

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